Supported by IEEE CIS ISATC Task Force on Transfer Learning and Transfer Optimization
Memetic Computing (MC) represents a broad generic framework using the notion of meme(s) as units
information encoded in computational representations for the purpose of problem-solving. In the
literature, MC has been successfully manifested as memetic algorithm, where meme has been
perceived as individual learning procedures, adaptive improvement procedures or local search
operators that enhance the capability of population based search algorithms. More recently,
manifestations of meme in the forms such as knowledge building-block, decision tree, artificial
neural works, fuzzy system, graphs, etc., have also been proposed for efficient problem-solving.
These meme-inspired algorithms, frameworks and paradigms have demonstrated with considerable
in various real-world applications.
The aim of this special session on memetic computing is to provide a forum for researchers in this field to exchange the latest advances in theories, technologies, and practice of memetic computing. The scope of this special session covers, but is not limited to:
Supported by IEEE CIS ECTC Task Force on Data-Driven Evolutionary Optimization of Expensive Problems
Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face
when solving time-consuming problems, as typically these approaches require thousands of
evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are
computationally cheap, have in recent years gained in popularity in assisting meta-heuristic
optimization, by replacing the compute-expense/time-expensive problem during phases of the
search. However, due to the curse of dimensionality, it is very difficult, if not impossible to
train accurate surrogate models. Thus, appropriate model management techniques, memetic
and other schemes are often indispensable. In addition, modern data analytics involving advance
sampling techniques and learning techniques such as semi-supervised learning, transfer learning
active learning are highly beneficial for speeding up evolutionary search while bringing new
insights into the problems of interest. This special session aims at bringing together
from both academia and industry to explore future directions in this field.
The topics of this special issue include but are not limited to the following topics:
Supported by IEEE CIS ISATC Task Force on Hyper-Heuristics
Computational intelligence systems play an imperative role in solving real world complex problems
industry. These systems have contributed to many facets of industry including data mining,
transportation, health systems, computer vision, computer security, robotics, software
scheduling, and amongst others. Computational intelligence systems employ one or more
intelligence techniques such as neural networks, fuzzy logic, genetic algorithms, multi-agent
approaches and rule-based systems. Implementation of these techniques require a number of design
decisions to be made, e.g. what architecture to use, what parameter values to use, and
problem specific operators. It may also be necessary to employ a hybrid system combining
to solve a problem. This makes the development of computational systems time consuming,
extensive expertise, and many man hours. Consequently, there have been a number of initiatives
automate these processes.
There has been a fair amount of research into parameter tuning and control, automating neural network architecture design, inducing fuzzy functions, rule-based systems and multi-agent architectures, amongst others. The aim of this special session is to examine recent developments in the field and future directions including the challenges and how these can be overcome.
The topics covered include, but are not limited to, the use of EC for the following:
Supported by IEEE CIS Games Technical Committee
Games are an ideal domain to study computational intelligence (CI) methods because they provide
affordable, competitive, dynamic, reproducible environments suitable for testing new search
algorithms, pattern-based evaluation methods, or learning concepts. Games scale from simple
for developing algorithms to incredibly hard problems for testing algorithms to the limit. They
also interesting to observe, fun to play, and very attractive to students. Additionally, there
great potential for CI methods to improve the design and development of both computer games as
as tabletop games, board games, and puzzles. This special session aims at gathering leaders and
neophytes in games research as well as practitioners in this field who research applications of
computational intelligence methods to computer games.
In general, papers are welcome that consider all kinds of applications of methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, rolling horizon algorithms, MCTS, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.).
Examples include but are not limited to:
Supported by IEEE CIS ETTC Task Force on Creative Intelligence
Evolutionary computation (EC) techniques, including genetic algorithm, evolution strategies,
programming, particle swarm optimization, ant colony optimization, differential evolution, and
memetic algorithms have shown to be effective for search and optimization problems. Recently, EC
deep neural networks gained several promising results and become important tools in
creativity, such as in music, visual art, literature, architecture, and industrial design.
The aim of this special session is to reflect the most recent advances of EC for Music, Art, and Creativity, with the goal to enhance autonomous creative systems as well as human creativity. This session will allow researchers to share experiences and present their new ways for taking advantage of CI techniques in computational creativity. Topics of interest include, but are not limited to, EC in the following aspects:
In multiobjective optimization problems, there may exist two or more distinct Pareto optimal sets
(PSs) corresponding to the same Pareto Front (PF). These problems are defined as multimodal
multiobjective optimization problems (MMOPs). Arguably, finding one of these multiple PSs may be
sufficient to obtain an acceptable solution for some problems. However, failing to identify more
than one of the PSs may prevent the decision maker from considering solution options that could
bring about improved performance.
The aim of this special session is to promote the research on MMO and hence motivate researchers to formulate real-world practical problems. Given that the study of multimodal multiobjective optimization (MMO) is still in its emerging stages, although many real-word applications are likely to be amenable to treatment as a MMOP, to date the researchers have ignored such formulations.
This special session is devoted to the novel approaches, algorithms and techniques for solving MMOPs. The main topics of the special session are:
The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which
based on the collective behavior of human being, that is, the brainstorming process. There are
major operations involved in BSO, i.e., convergent operation and divergent operation. A “good
enough” solution could be obtained through recursive solution divergence and convergence in the
search space. The designed optimization algorithm will naturally have the capability of both
convergence and divergence. The BSO algorithm can be seen as a combination of swarm intelligence
data mining techniques. Every individual in the brain storm optimization algorithm is not only a
solution to the problem to be optimized, but also a data point to reveal the landscapes of the
problem. The swarm intelligence and data mining techniques can be combined to produce benefits
and beyond what either method could achieve alone.
This special session aims at presenting the latest developments of BSO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special session. Potential topics include, but are not limited to:
The volume of cybercrime is increasing daily with increasing use of the internet for email and
media purposes. The use of neural networks for tackling cybercrime is an active area of
For example, conventional neural network-based solutions have been proposed to detect image
tampering, source camera attribution of an anonymous crime image, explicit content detection,
detection, etc. Recently, researchers are focusing more on unsupervised solutions. New types of
cybercrimes are also emerging (e.g., deepfake video) which may, in turn, require new approaches.
Interestingly, the role of evolutionary computing in tackling cybercrime is relatively
This special session aims to bring together researchers from both academia and industry in the application of evolutionary computation and neural networks for combating cybercrime. This session also will welcome research which focuses on the risk of a neural network for spreading new kind of cybercrimes (e.g., deepfake videos) and evolutionary computing for creating new types of malware (e.g., polymorphic and metamorphic viruses). The session will attract researchers working in cybersecurity, evolutionary computation, and neural networks. Of particular interest will be research that combines evolutionary computing with neural network approaches. The main topics of this special session include, but are not limited to, the following:
Biomedical data contains several challenges in data analysis, including high dimensionality,
imbalance and low numbers of samples. There is also a need to explore big data in biomedical and
healthcare research. An increasing flood of data characterises human health care and biomedical
research. Healthcare data are available in different formats, including numeric, textual
signals and images, and the data are available from different sources. An interesting aspect is
integrate different data sources in the data analysis process which requires exploiting the
domain knowledge from available sources. The data sources can be ontologies, annotation
repositories, and domain experts’ reports.
This special session aims to bring together the current research progress (from both academia and industry) on data analysis for biomedical and healthcare applications. It will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using the data mining effectively. Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and healthcare applications.
The main topics of this special session include, but are not limited to, the following:
With the explosion of data generation, getting optimal solutions to data driven problems is
increasingly becoming a challenge, if not impossible. Pigeon Inspired Optimization (PIO) is a
recently developed intelligent bio-inspired algorithm and become popular to deal with a variety
optimization problems. It is capable of addressing highly complex problems to provide working
solutions in time, especially with dynamic problem definitions, fluctuations in constraints,
incomplete or imperfect information and limited computation capacity.
This special session aims at presenting the latest developments of PIO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special Session. Potential topics include, but are not limited to:
Supported by IEEE CIS ETTC
This special session focuses on both practical and theoretical aspects of Evolutionary Scheduling and
Combinatorial Optimization. Examples of evolutionary methods include genetic algorithm, genetic
programming, evolutionary strategies, ant colony optimization, particle swarm optimization,
based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning
evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include
machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary
algorithms for reinforcement learning and transfer learning.
We welcome the submissions of quality papers that effectively use the power of EC techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimisation problems are also highly encouraged.
Topics of interest include, but not limited to:
Supported by IEEE CIS ETTC Task Force on Operations Research and Management Sciences
The field of evolutionary multi-objective optimization has developed rapidly over the last 20 years,
the design of effective algorithms for addressing problems with more than three objectives (called
many-objective optimization problems, MaOPs) remains a great challenge. First, the ineffectiveness
the Pareto dominance relation, which is the most important criterion in multi-objective
results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the
conflict between convergence and diversity, along with increasing time or space requirement as well
parameter sensitivity, has become key barriers to the design of effective many-objective
algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious
difficulties in algorithms' performance investigation and comparison. All of these suggest the
need of new methodologies designed for dealing with MaOPs, new performance metrics and test
tailored for experimental and comparative studies of evolutionary many-objective optimization (EMaO)
List of topics, but are not limited to:
Supported by IEEE CIS ISATC Task Force on Intelligence Systems for Health
Worldwide, the healthcare industry would continue to thrive and grow, because diagnosis, treatment,
disease prevention, medicine, and service affect the mortal rates and life quality of human beings.
key issues of the modern healthcare industry are improving healthcare quality as well as reducing
economic and human costs. The problems in the healthcare industry can be formulated as scheduling,
planning, predicting, and optimization problems, where evolutionary computation methods can play an
important role. Although evolutionary computation has been applied to scheduling and planning for
system and pharmaceutical manufacturing, other problems in the healthcare industry like decision
in computer-aided diagnosis and predicting for disease prevention have not properly formulated for
evolutionary computation techniques, and many evolutionary computation techniques are not well-known
the healthcare community. This special session aims to promote the research on evolutionary
methods for their application to the healthcare industry.
The topics of this special session include but are not limited to the following topics:
Supported by IEEE CIS ETTC
Services computing is becoming more and more prominent in the Internet environment with the rapid
of services available on the internet. Cloud computing has become a scalable services consumption
delivery platform in the field of Services Computing. This raises issues for service providers such
Web service composition and service location allocation, resource allocation and scheduling, etc.
Computational Intelligence (CI) has been successfully applied to many challenging real-world
This special session aims to solve the service and cloud computing problems with CI techniques,
all different evolutionary computation paradigms such as GA, GP, EP, ES, MA, LCS, PSO, ACO, DE, and
The scope of this special session includes both new theories and methods on how to solve the challenging services computing and cloud computing problems. Topics of interest include, but not limited to:
Evolutionary Machine Learning (EML) explores technologies that integrate machine learning (e.g., neural networks, decision trees, fuzzy systems, reinforcement learning) with evolutionary computation for tasks including optimization, classification, regression, and clustering. Since machine learning contributes to parameter learning while evolutionary computation contributes to model/parameter optimization, one of the fundamental interests in EML is a management of interactions between learning and evolution to produce a system performance that cannot be achieved by either of these approaches alone. Historically, this research area was called Genetics-Based Machine Learning (GBML) and it was concerned with learning classifier systems (LCS) with its numerous implementations. More recently, EML has emerged as a more general field than GBML. It is consequently a broader, more flexible and more capable paradigm than GBML. From this viewpoint, the aim of this special session is to explore potential EML technologies and clarify new directions for EML to show its prospects. For this purpose, this special session focuses on, but is not limited to, the following areas in EML:
Many of the tasks carried out in data mining and machine learning, such as feature subset selection,
associate rule mining, model building, etc., can be transformed as optimization problems. Thus it is
very natural that Evolutionary Computation (EC), has been widely applied to these tasks in the
data mining (DM) and machine learning (ML), as an optimization technique. On the other hand, EC is a
class of population-based iterative algorithms, which generate abundant data about the search space,
problem feature and population information during the optimization process. Therefore, the data
and machine learning techniques can also be used to analyze these data for improving the performance
The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice. We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:
To shape a low carbon energy future has been a crucial and urgent task under Paris Global Agreement.
Numerous optimisation problems have been formulated and solved to effectively save the fossil fuel
and relief energy waste from power system and energy application side. However, some key problems
strong non-convex, non-smooth or mixed integer characteristics, leading to significant challenging
issues for system operators and energy users. Evolutionary computation is immune from complex
modeling formulation, and is therefore promising to provide powerful optimisation tools for
intelligently and efficiently solving problems such as smart grid and various energy systems
to reduce carbon consumptions.
This special session intends to reflect the state-of-the-art advances of evolutionary optimisation approaches for solving emerging problems in complex modern power and energy system. The submissions are encouraged to be focus on smart grid scheduling with integration of new participants such as renewable generations, plug-in electric vehicles, distribution generations and energy storages, multiple time-spacial energy reductions and other energy optimisation topics. Potential submission topics include:
Supported by IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction
In machine learning and data mining, the quality of the input data, i.e. feature space, is a key for
success of any algorithm. Feature selection, feature extraction or construction and dimensionality
reduction are important and necessary data pre-processing techniques to increase the quality of the
feature space. However, they are challenging tasks due to the large search space and feature
interactions. This special session aims to use Evolutionary Computation for feature reduction,
ALL different evolutionary computation paradigms. Authors are invited to submit their original and
unpublished work to this special session.
Topics of interest include but are not limited to:
Supported by IEEE CIS ISATC Task Force on Transfer Learning & Transfer Optimization
Transfer learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain,
another domain, i.e. the target domain. Transfer learning has recently emerged as a popular learning
framework in data mining and machine learning. Evolutionary computation techniques have been
successfully applied to many real-world problems, and started to be used to solve transfer learning
transfer optimisation tasks.
The aim of this special session is to investigate in both the new theories and methods on how transfer learning and optimisation can be achieved with evolutionary computation, and how transfer learning can be adopted in evolutionary computation. Authors are invited to submit their original and unpublished work to this special session.
Topics of interest include but are not limited to:
Supported by IEEE CIS ECTC Task Force on Nature-Inspired Constrained Optimization
In their original versions, nature-inspired algorithms for optimization such as evolutionary algorithms (EAs) and swarm intelligence algorithms (SIAs) are designed to sample unconstrained search spaces. Therefore, a considerable amount of research has been dedicated to adapt them to deal with constrained search spaces. The objective of the session is to present the most recent advances in constrained optimization using different nature-inspired algorithms. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:
Supported by IEEE CIS Task Force on Theory of Bio-inspired Computing
The area of runtime analysis for bio-inspired computing techniques provides new insights into the
behaviour of these methods for solving optimization problems. The theoretical analysis of these
algorithms includes studying the runtime complexity with respect to the input size and/or other
parameters of the instance of the problem until reaching an optimal or approximate solution of the
problem. Rigorous analysis of these algorithms helps us in designing more efficient algorithms.
Moreover, investigating the effect of different parameters of the studied algorithms lead to more
efficient parameter tuning for these algorithms. Furthermore, studying the theoretical behaviour of
bio-inspired algorithms with respect to the characteristics of the studied problem is beneficial in
choosing the right algorithm for solving each instance of the problem.
The purpose of this special session is to bring together people working on theoretical analysis of bio-inspired computing techniques. We aim to provide a forum for the researchers in this field to discuss the latest outcomes and new directions in the theory of bio-inspired algorithms. Topics of interest include, but are not limited to:
Supported by IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering
Bioinformatics and Bioengineering (BB) are interdisciplinary scientific fields involving many
computer science, engineering, mathematics, and statistics. Bioinformatics is concerned with the
development and application of computational methods for the modeling, retrieving and analysis of
biological data, whilst Bioengineering is the application of engineering techniques to biology so as
create usable and economically viable products.
Bioinformatics and Bioengineering are relatively new fields in which many challenges and issues can be formulated as (single and multiobjective) optimization problems. These problems span from traditional problems, such as the optimization of biochemical processes, construction of gene regulatory networks, protein structure alignment and prediction, to more modern problems, such as directed evolution, drug design, experimental design, and optimization of manufacturing processes, material and equipment.
The main aim of this special session is to bring together both experts and new-comers working on Optimization, Learning and Decision-Making in Bioinformatics and Bioengineering to discuss new and exciting issues in this area. The topics are, but not limited to, the following
Evolutionary Computation (EC) techniques are widely used in Internet of Everything (IoEt)
such as Internet of Things, Fog Computing, Edge Computing, and Cloud Computing towards sustainable
computing infrastructure at different levels. A large amount of effort is being put toward achieving
distributed artificial intelligence. Distributed Artificial Intelligence is a method to enable
learning, planning, and decision-making problems in a decentralized fashion. It is able to execute
scale computation through distributed computing resources. These properties allow it to solve
that require the processing of very large data sets. This approach, when put together with the idea
Internet of Everything, opens up a new world of applications of artificial intelligence in a
It is clear that EC is going to play a huge role in the lives of the average human being. With the ongoing research in fields like distributive computing, fuzzy logic, neural networks etc, we can also be sure that the intelligence that will run the world will be far more advanced and heuristic than the kind we have today.
The main topics of this special session include, but are not limited to, the following:
Symbolic modeling is the process of developing symbolic descriptions/mathematical models to capture
structure of the data and make an accurate prediction. In evolutionary computation, symbolic
can be achieved by a set of techniques including but not only limited to genetic programming and
learning classifier systems.
The theme of this special session is to promote evolutionary computation for symbolic modelling. This involves contributions to the state-of-the-art symbolic modelling techniques through either theoretical or algorithmic work investigating and enhancing the learning, the generalisation, the interpretability, the efficiency and the robustness of the techniques. This also involves methods to handle complex large-scale datasets and high-dimensional datasets, e.g. instance sampling, feature selection and feature construction. Novel applications are also important and interesting to explore. Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:
Numerous industries and service sections are now striving to embrace the latest AI technologies. As a result, research in scheduling and network design problems is getting increasingly popular. Most of these problems, unfortunately, are NP-Hard and cannot usually be solved by exact approaches. Therefore, significant research attention has been attracted on exploring techniques in hybridizing Computational Intelligence (including evolutionary computation, neural networks, swarm intelligence, fuzzy logic) with classic integer programming techniques like branch and bound, column generations, branch and pricing, etc. This special session aims to explore recent advances in this area. Topics include, but are not limited to, the following:
Many of industrial and research databases suffer from an unavoidable problem of data incompleteness.
big data era, data is generated almost everywhere; therefore, the more data is collected, the more
frequent the data suffers from missing values. Missing values not only cause the non-applicability,
also cause the loss of effectiveness and efficiency of almost all data mining algorithms.
Evolutionary computation (EC) techniques have been successfully applied to solve and improve data mining tasks. However, EC techniques have been mainly used for data mining with complete data and also, but with less effort with missing data. Hence, this special session aims to encourage information exchange and discussion between researchers with an interest in the use of any EC technique for handling missing data in data mining and machine learning. Topics of interest include but are not limited to, the use of any EC technique for handling missing data in data mining:
The application of complex networks to evolutionary computation (EC) has received considerable
from the EC community in recent years. The most well-known study should be the attempt of using
networks, such as small-world networks and scale-free networks, as the potential population
in evolutionary algorithms (EAs). Structured populations have been proposed to as a means for
the search properties because several researchers have suggested that EAs populations might have
structures endowed with spatial features, like many natural populations. Moreover, empirical results
suggest that using structured populations is often beneficial owing to better diversity maintenance,
formation of niches, and lower selection pressures in the population favouring the slow spreading of
solutions and relieving premature convergence and stagnation. Moreover, the study of using complex
networks to analyse fitness landscapes and designing predictive problem difficulty measures is also
attracting increasing attentions. On the other hand, using EAs to solve problems related to complex
networks, such as community detection, is also a popular topic.
This special session seeks to bring together the researchers from around the globe for a creative discussion on recent advances and challenges in combining complex networks and EAs. The special session will focus on, but not limited to, the following topics:
Supported by IEEE CIS TF on Evolutionary Computation in Dynamic and Uncertain Environments
Many real-world optimization problems are subject to dynamism and uncertainties that are often
to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of
simulation/ measurement errors or approximation errors (in the case where surrogates are used in
of the computationally expensive high-fidelity fitness function). In addition, the design variables
environmental conditions can be perturbed, or they change over time. The tools to solve these
and uncertain optimization problems (DOP) should be flexible, able to tolerate uncertainties, fast
allow reaction to changes and adaptation. Moreover, the objective of such tools is no longer to
locate the global optimum solution, but to continuously track the optimum in dynamic environments,
find a robust solution that operates properly in the presence of uncertainties.
This special session aims at bringing together researchers from both academia and industry to review the latest advances and explore future directions in this field. Topics of interest include, but are not limited to:
Supported by IEEE CIS ETTC Task Force on Artificial Immune Systems
The biological immune system is a powerful defense system that protects an organism itself from
and danger. The biological immune system is a kind of complex, adaptive, dynamic and distributive
systems with abilities of learning, memory, self-organization, feature extraction and pattern
recognition. The Artificial Immune System, also known as Immune Computation, is a relatively new
in the computational intelligence community, which is inspired by the structure, functions, models
information processing mechanism of biological immune system. The Artificial Immune System is a fast
developing research area. Nowadays, the scope of this research area ranges from modeling to
of the biological immune system, to the development of novel engineering solutions to complex
This special session aims to focus on the state-of-the-art research on immune-inspired algorithms and the new conceptual models for understanding the dynamics that underlie the immune system. Topics of interest include, but are not limited to:
Supported by IEEE CIS Task Force on the Ethical and Social Implications of Computational Intelligence
Computational Intelligence (CI) can provide great benefits to society but also will introduce some
challenges. For example, are CI systems used for marking student assignments capable of bias?
is the current legal framework capable of dealing with the repercussions of decisions made by CI
on matters such as finance, medical treatments or autonomous vehicle collision avoidance. This
session aims to discuss solutions to some of these challenges, what safeguards might be required
technologically and legally) and how we can better present the benefits of CI to the wider
Topics of interest include, but are not limited to:
A Smart City is an urban area that uses different types of electronic data collection sensors to
information which is used to manage assets and resources efficiently. It integrates information and
communication technology (ICT), and various physical devices connected to the network (the Internet
things or IoT) to optimize the efficiency of city operations and services. A smart city is powered
“smart connections” for various items such as street lighting, smart buildings, distributed energy
resources (DER), data analytics, and smart transportation.
There are various complex optimization problems in Smart City. The aim of this special session is to bring together researchers from both academia and industry in the application of evolutionary computation for solving various optimization problems in smart city. The main topics of this special session include, but are not limited to, the following:
Cooperative Evolutionary Computation refers to the area of having multiple evolutionary algorithmic
instances cooperating with each other to solve an optimization problem. Cooperation could be
as implicit or explicit. In implicit decomposition, multiple instances implicitly tackle different
of the search domain due to different initialization, parameter settings, etc. In explicit
decomposition, each instance operates in a dedicated subdomain either by dividing the entire domain
between instances or dividing the problem variables (i.e. cooperative coevolution). Moreover,
cooperative algorithms could be classified as homogeneous (multiple instances of the same
algorithm) or heterogeneous (instances of different evolutionary algorithms). Many cooperative
algorithms have produced remarkably effective solutions, with a faster speed of convergence, to
continuous, large-scale, discrete, combinatorial and multi-objective problems in many fields.
This special session aims at presenting the latest developments of cooperative evolutionary computation techniques, exchanging new ideas and discussing open research questions and future directions. Original contributions that provide novel theories, frameworks, and applications to this topic are very welcome. Potential topics include, but are not limited to:
Supported by IEEE CIS Computational Finance and Economics TC
Computational Finance and Economics covers a wide area of topics and techniques. The arrival of new computational methods, especially from Evolutionary Computation (EC), continually pushes the boundaries of the field outwards. That, together with the advances in available hardware, have contributed to a growing interest in applying EC techniques to solve different financial and economic problems. This Special Session is dedicated to the application of Evolutionary Computation methodologies to such problems. We welcome papers from any algorithm from the EC field, as well as hybrid EC methods. Applications include (but not limited to):
The smart city concept integrates information and communication technology, and various physical
connected to the cloud network to optimize the efficiency of city operations and services and
citizens. Therefore, most of real-world problems in the smart city are multimodal interface problems
and/or multi-objective optimization problems involving several conflicting objectives. On the other
hand, cloud systems may even offer tens of thousands of virtual machines, terabytes of memories and
exa-bytes of storage capacity. Current trend toward many-core architecture increases the number of
even more dramatically.
In this special session, we will discuss new parallel and distributed evolutionary computation (EC) in the smart city era, in term of both reduction in execution time and improvements in accuracy of the achieved solutions. Topics of interest include, but are not limited to, EC in the following aspects:
Supported by IEEE CIS FSTC Task Force on Adaptive and Evolving Fuzzy Systems
Granular computing (GC) focuses on the knowledge representation and reasoning with information
and fuzzy sets and rough sets are two crucial branches of GC. Evolutionary computation for granular
computing emphasizes the utility of different evolutionary algorithms to various facets of granular
computing, ranging from theoretical analysis to real-life applications. The main motivation for
evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are
and adaptive search methods, which can perform a global search in the space of candidate solutions.
benefits of exploring the combination of granular computing and evolutionary computation will have
impact in multiple research disciplines and industry domains, including transportation,
social network, medical health, and so on.
The goal of this special section aims at providing a specific opportunity to review the state-of-the-art of evolutionary computation for granular computing. We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:
Deep learning has shown significantly promising performance in addressing real-world problems. The
achievements of such algorithms owe to its deep structures. However, designing an optimal deep
requires rich domain knowledge on both the investigated data and the general data analysis domain,
is not necessarily held by the end-users. In addition, the problem of searching for the optimal
structure could be non-convex and non-differentiable, and existing accurate methods are incapable of
well addressing it.
Evolutionary computation (EC) approaches have shown superiority in addressing real-world problems due largely to their powerful abilities in searching for global optima, and requiring no rich domain knowledge. However, most of the existing EC methods currently work only on relatively shallow structures, and cannot provide satisfactory results in searching for deep structures. In this regard, evolutionary deep learning, would be a great research topic.
The theme of this special session aims to bring together researchers investigating methods and applications in evolutionary deep learning.
Many real-world engineering optimization problems not only require the simultaneous optimization of a
number of objective functions, but also need to track the changing optimal solutions. These problems
called: Dynamic multi-objective optimization (DMOO) problems. Here, where either the objective
or the constraints change over time, an optimization algorithm should be able to find, and track the
changing set of optimal solutions and approximate the time-varying true Pareto front. Therefore, the
DMOO algorithm also has to deal with the problems of a lack of diversity and outdated memory.
EThe main goal of this session is to emphasize the newest techniques in solving dynamic multi-objective optimization problems and handling current issues. Due to the novelty of DMOO, the session more concentrates on combining DMOO with other hot topics, such as deep learning. Therefore, session aims at providing a forum for researchers in the area of DMOO to exchange new ideas and submit their original and unpublished work. Topics of interest include, but are not limited to:
This special session is concerned about the Self-Organizing Migrating Algorithm (SOMA), belonging to
class of swarm intelligence techniques. Compare to other prominent swarm intelligence paradigms, as
example Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly, and so on, SOMA
distinguished by competitive-cooperative phases, inherent self-adaptation of movement over the
space, as well as by discrete perturbation mimicking the mutation process known from the classical
evolutionary computing techniques. The SOMA perform significantly well in both continuous as well as
discrete domains. The SOMA has been used successfully on various tasks as the real-time plasma
control, aircraft wings optimisation, chaos control, large scale, combinatorial and permutative
This special session is concern about original research papers discussing new results on and with SOMA, as well as its novel improvements tested on widely accepted benchmark tests. This session aims to bring together people from fundamental research, experts from various applications of SOMA to develop mutual intersections and fusion. Also, a discussion of possible hybridisation amongst them as well as real-life experiences with computer applications will be carried out to define new open problems in this interesting and fast-growing field of research. The special session will focus on, but not limited to, the following topics:
Most of modern engineering and scientific applications are concerned with big optimization problems in terms of number of variables (more than thousands), objectives, constraints, data, uncertainties and so on. The goal of this special session is to come up with cutting-edge evolutionary and meta-heuristic approaches to deal with big optimization problems such as parallel design and implementation, decomposition methods, model-based optimization, surrogate-based optimization, cross-domain, exascale and ultra-scale optimization, deep learning architectures, optimization under uncertainties, and mixed optimization. This special session focuses on, but not limited to, the following areas:
Recently with the development of IoT and high-performance computing, the importance of cyber-physical
systems that connect the real and a virtual world is increasing. For example, Japan government
"Society 5.0" as a key concept for the future. "Society 5.0" describes a future world where smarter
systems work by communicating between cyber and physical spaces. System modeling, signal processing,
filtering are the fundamental techniques in the cyber-physical system and an interaction between the
real and a virtual world. Such system techniques should have the adaptability to the nonlinearity of
physical system and time-varying environments. The evolutionary approach will be a successful
methodology in this fields.
This special session promotes the theoretical discussion on applications of evolutionary computation to control, modeling and filtering for dynamical systems and cyber-physical systems, developments of novel filtering algorithm using evolutionary computation, and data assimilation methods for simulation studies. The design of experiments by using evolutionary algorithms is also important for this fields to build efficient simulation scenarios.
Authors are invited to submit their original work including the following (but not limited to) topics:
Supported by IEEE CIS Task Force on Decomposition-based Techniques in Evolutionary Computation
Decomposition-based Evolutionary Multi-objective Optimization (DEMO) encompasses any technique, concept or framework that takes inspiration from the “divide and conquer” paradigm, by breaking a multi-objective optimization problem into several subproblems. This simple idea, which is somewhat standard in computer science, allows to open up new research perspectives and challenges in both the fundamental level of our understanding of multi-objective problems and concerning designing and implementing new efficient algorithms for solving them. Many different DMOEAs variants have been proposed, studied and applied to various application domains in recent years. However, DMOEAs are still in their very early infancy, since only a few basic design principles have been established compared to the vast body of literature dedicated to other well-established approaches (e.g., Pareto-, indicator-based techniques, etc.). Thus, the topics of interest include (but are not limited to) the following aspects:
Supported by IEEE CIS Task Force on Decomposition-based Techniques in Evolutionary Computation
Fireworks Algorithm (FWA) is a new swarm-based optimization algorithm which has different cooperation
framework and search manner compared to other SI algorithms, such as Particle Swarm Optimization,
Colony Optimization, and Genetic Algorithm. Locally, populations called fireworks exploit local
landscape by a simple sampling method called explosion operation. Globally, fireworks exchange
information and collaboratively decide parameters of their explosion. FWA achieved overwhelming
on both benchmark objective functions and real-world problems. Recent research includes many
variants and huge amount of successful applications. FWA framework has revealed competitive
with other SI optimization methods.
The main aim of this special session is to gather both experts’ experience and new-comers’ innovations of firework algorithm and its applications. We are expecting researches on theoretical analysis and improvement of FWA and application of all kinds of practical situations. Full papers are invited on recent advances in the development of FWA. The session seeks to promote the discussion and presentation of novel works related (but not limited) to the following issues:
Supported by IEEE CIS ISATC Task Force on Intelligent Adaptive Fault Tolerant Control, Reliability, and Optimization
Swarm intelligence, as a crucial aspect of the artificial intelligence domain, has become an increasingly important modern computational intelligence method in artificial intelligence and computer science. In swarm intelligence, the nascent collective intelligence of groups of simple agents possess a powerful global search capability, and has been demonstrated to be able to determine the optimal solution within a rational time by numerous study fields using swarm intelligence algorithms, such as GA, MA, ACO, PSO, ABC, SSO, etc. Swarm intelligence algorithms play a paramount role in optimizing the increasing problems in related complex systems. Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. This special session will provide a cardinal opportunity to present the latest scientific results and methods on the collaboration of Swarm Intelligence in Operations Research, Management Science and Decision Making, to discuss and exchange the latest developments in Swarm Intelligence, and to explore the future directions in Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:
Smart logistics refers to the efficient and effective design, planning and control of the supply
chain processes though intelligent technologies, such as software to improve the design of networks,
software to automate scheduling, routing, and dispatching, material handling systems, etc.
Respectively, the relevant research methods involve clustering, stochastic (dual) dynamic
programming, planning and optimization. In recent years, evolutionary computation (EC) techniques
have been introduced to the area of logistics. Examples include applying single-objective and
multi-objective evolutionary algorithms to facility layout decision problems and vehicle routing
This special session aims at presenting the latest research on EC applications to logistics. Real-world applications of EC on logistics are highly recommended. The topics include but are not limited to:
By bringing together multiple energy systems, including electricity, thermal sources and fuels, and
other critical infrastructures, such as transportation, water and communication, we can improve
their efficiency, reliability and resiliency. Currently, most energy systems and critical
infrastructures are operated independently. Multiple energy systems integration (MESI) focus on the
coordination and optimization of those energy systems and critical infrastructures in the operation
and planning stages. Various operating conditions, include normal operations, typical interruptions
and extreme events, are considered to maximize the value of each unit of energy we use and enhance
the ability of those systems and infrastructures to withstand and recover from typical and
catastrophic disturbances. Most optimization problems we encounter in MESI could be nonconvex and
contain large number of integer variables which cannot be solved efficiently using existing
mathematic programming methods. This special session aims to promote the research on Evolutionary
Computation methods for their application to MESI.
Topics of interest include, but are not limited to:
Supported by IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing
The fields of computer vision (CV) and image processing (IP) have tried to automate tasks that the
human visual system can do, with the aim of gaining a high-level understanding of images and videos.
CV algorithms have been successfully applied to a large number of real-world problems ranging from
remote sensing to medical image analysis, video surveillance, human-robot interaction, and
computer-aided design. In turn, evolutionary computation (EC) methods have shown to be more
efficient than classical optimization approaches for discontinuous, non-differentiable, multimodal
and noisy problems.
The aim of this special session is to provide a forum for researchers to exchange the latest advances in theories, technologies, and practice of both research fields (EC and CV/IP). The scope of this special session covers, but is not limited to, the application of EC paradigms to:
Supported by IEEE CIS Task Force on Evolvable Hardware (http://www-users.york.ac.uk/~mt540/ieee-tf-ehw)
Evolvable systems encompass understanding, modelling and applying biologically inspired mechanisms to
physical systems. Application areas for bio-inspired algorithms include the creation of novel
physical devices/systems, novel or optimised designs for physical systems and for the achievement of
adaptive physical systems. Having showcased examples from analogue and digital electronics,
antennas, MEMS chips, optical systems, carbon nanotubes as well as quantum circuits in the past, we
are looking for papers that apply techniques and applications of evolvable systems to these hardware
Within the scope of this special session are
Evolvable Systems Techniques
Evolvable Systems Applications
During last years, a wide range of population-based meta-heuristics have been proposed with the aim of dealing not only with benchmark optimization problems, but also with real-world applications. Population-based approaches keep a set of solutions with the aim of exploring the search space in an efficient way. Usually, a diverse set of solutions is maintained, meaning that several regions are explored simultaneously. However, one common problem of population-based meta-heuristics is that for some test cases they might exhibit a tendency to converge quickly towards some regions. One of the most frequent problems that these types of meta-heuristics have to deal with is premature convergence, which arises when every member of the population is located at a sub-optimal area of the decision space from where they cannot escape. A significant number of methods have been proposed in order to preserve the diversity in a set of solutions. This special session aims to attract the most relevant advances produced in the following topics, including but not limited to:
As an underpinning science/engineering discipline, evolutionary optimisation has contributed
significantly to the growth of advanced manufacturing systems globally. Topics such as the ones of
multi-objective process optimisation, decision-making, real-time performance optimisation are
pivotal for the realisation of the Industry 4.0 vision. Whether the manufacturing application is
about digitisation systems, robotics, digital manufacturing or fundamental understanding of advanced
processes and complex materials, evolutionary optimisation is ideally placed to offer algorithms and
methods to address challenges specific to the manufacturing sector.
In this session, we invite contributions that demonstrate new applications, results, as well as new algorithms that address challenges specific to advanced manufacturing systems. Specific topics of interest include, but not limited to:
Supported by IEEE CIS Task Force on Quantum Computing
Quantum computing (QC) represents a broad topic encompassing a large number of approaches,
technologies and techniques focusing on the usage, application, design and understanding of quantum
computing systems. Evolutionary Computing (EC) has been on several occasions directly linked to
quantum computing such as quantum evolutionary computation or evolutionary design for quantum
computer design, etc. Because quantum computing evolves in the very large Complex Hilbert space,
evolutionary methods are a prime tool for exploration and exploitation of quantum properties.
The aim of this special session on quantum and evolutionary computing is to provide a platform for researchers of various fields to discuss the latest advances in related fields, technologies and approaches linking and using quantum and evolutionary approaches. The scope of this special session covers among others but not limited to the following topics:
Research work is welcome concerning complex real-world applications of evolutionary computation (EC)
in the energy domain. The problems can be focused on different parts of the energy chain (e.g.,
heating, cooling, and electricity supply) and different consumer targets (e.g., residential or
industrial level). Problems dealing with uncertainty, dynamic environments, many-objectives, and
large-scale search spaces are important for the scope of this special session. This special session
aims at bringing together the latest applications of EC to complex optimization problems in the
energy domain. Besides, this special session is linked to the competition on “Evolutionary
Computation in Uncertain Environments: A Smart Grid Application”. Therefore, participants are also
welcome to submit the results of their algorithm to our session.
Topics must be related to EC in the energy domain including, but not limited to:
Supported by IEEE CIS ISATC Task Force on Intelligent Network Systems (TF-INS)
Transportation serves as an important task in modern human life and industry activities. Optimization
for intelligent transportation systems has shown to be a difficult problem. The worldwide division
of labor, the connection of distributed centers, and the increased mobility of individuals,
furthermore, lead to an increased demand for efficient solutions to solve the problems in
transportation networks. Evolutionary computation plays a significant role and has gained promising
results in optimization of transportation networks.
The aim of this special session is to promote research and reflect the most recent advances of evolutionary computation in in intelligent transportation systems. Topics of interest include, but are not limited to:
Supported by IEEE CIS ETTC Task Force on Aerospace Science
In Space and Aerospace Science and Engineering, many applications require the solution of global single and/or multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities. From global trajectory optimization to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, computational intelligence (CI) techniques have become an important – and in many cases inevitable – tool for tackling these kinds of problems, providing useful and non-intuitive solutions. This special session intends to collect many, diverse efforts made in the application of computational intelligence techniques, or related methods, to aerospace problems. In particular, evolutionary methods specifically devised, adapted or tailored to address problems in space and aerospace applications or evolutionary methods that were demonstrated to be particularly effective at solving aerospace related problems are welcome.
Supported by IEEE CIS ETTC
Evolutionary computation (EC) algorithms have aroused great attentions from both the academic and
industrial communities in recent years due to their promising performance in many real-world
optimization problems. However, in the era of cloud computing and big data, many optimization
problems face the challenges of large scale & dynamic/uncertain, multimodal & many-objective, and
complexity & expensive fitness, which make traditional centralized EC algorithms based on a single
computer/computing resource result in low solution accuracy, slow convergence speed, and long
running time. In order to promote traditional centralized EC algorithms to solve the complicated
optimization problems in big data era, using distributed technology to enhance EC algorithms is a
Distributed EC (DEC) algorithms pose several new challenges: the design of DEC algorithms, the selection of distributed architectures, distributed resource scheduling method, and the deployment of the EC algorithms on distributed computing platform. This Special Session is to draw the attentions of researchers in both the communities of distributed technology and EC to exchange their latest advances in theories and technologies of EC, distributed technology, and the works on extending DEC approaches to real-world applications. Authors are invited to submit their original and unpublished work with the topics including, but not limited to:
Supported by IEEE CIS ETTC Task Force on Large Scale Global Optimization
In the past two decades, many evolutionary algorithms have been developed and successfully applied
for solving a wide range of optimization problems. Although these techniques have shown excellent
search capabilities when applied to small or medium sized problems, they still encounter serious
challenges when applied to large scale problems, i.e., problems with several hundreds to thousands
of variables. This is due to the Curse of dimensionality, as the size of the solution space of the
problem grows exponentially with the increasing number of decision variables, there is an urgent
need to develop more effective and efficient search strategies to better explore this vast solution
space with limited computational budgets. In recent years, research on scaling up EAs to large-scale
problems has attracted significant attention, including both theoretical and practical studies.
Existing work on tackling the scalability issue is getting more and more attention in the last few
This special session is devoted to highlight the recent advances in EAs for handling large-scale global optimization (LSGO) problems, involving single objective or multiple objectives, unconstrained or constrained, binary/discrete or real, or mixed decision variables. More specifically, we encourage interested researchers to submit their original and unpublished work on:
Supported by IEEE CIS ETTC Task Force on Collaborative Learning and Optimization
Differential evolution (DE) is one of the most promising research areas in evolutionary computation.
Over the past decades, DE-related algorithms have frequently demonstrated superior performance in
various challenging tasks. Meanwhile, the remarkable efficacy of DE in real-world applications
significantly boosts its popularity. However, the lack of systematic benchmarking of the DE-related
algorithms in different problem domains, the existence of many open problems in DE, and the
emergence of new application areas call for an in-depth investigation of DE.
This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this research area. Authors are invited to submit their original and unpublished work in the areas including but not limited to:
Multi-agent systems (MAS) are computerized systems composing of multiple interacting and autonomous
agents within a common environment of interest for problem-solving. The development of intelligent
agents that are capable of adapting to the complex or dynamic environment has attracted increasing
attentions over the past decades. In computational intelligence, evolutionary computation (EC), in
particular, has been shown to provide a reliable and flexible contender over traditional
mathematical approaches for solving complex optimization problems, especially if near global optimum
solutions are sought. According to the recent studies, EC based techniques, including evolutionary
algorithms, swarm intelligence, evolutionary reinforcement and transfer learning, are already
starting to show up in developing more significant intelligence among multiple agents in MAS.
Particularly, the intrinsic parallelism of natural evolution and the errors which are introduced due
to the physiological limits of the agents’ ability to perceive differences, could generate the
“growth” and “variation” of knowledge that agents have of the world, thus exhibiting high adaptivity
capabilities on solving complex and non-trivial problems.
Topics of interest include, but are not limited to:
Actionable Knowledge Discovery (AKD) is an importance task for data analysis in real-world applications. To reach that, multiple aspects, e.g., meta knowledge, domain knowledge, technique and business criteria, should be considered in algorithms, and hence the AKD in applications are optimization problems. Evolutionary Computation (EC) already shows its powerful ability for dealing with optimization problems in various fields and searching the global optimal solution. The aim of this special session is to provide a forum to disseminate and discuss recent and significant research effort on computational intelligence and other intelligence techniques for the AKD. This session is open to any high quality submission from researchers who work on mainly computational intelligence methods for the AKD. The scope of this section includes, but is not limited to the following topics:
Supported by IEEE CIS ISATC
Cybersecurity aims at preventing and detecting cyber attacks on Internet-connected systems which
include data, software, and hardware, in order to maintain the confidentiality, integrity, and
availability of those assets. Utilizing various evolutionary computation (EC) and machine learning
techniques to tackle numerous problems related to cybersecurity have received increasing attention
due to the success of such techniques to tackle problems in many other domains.
This interdisciplinary special session aims at providing a focused discussion forum for utilizing EC based techniques to automatically tackle different cybersecurity-related problems and other types of network-based attacks. It also aims at promoting both practical applications and theoretical development of EC techniques for information and network security domains. The scope of this special session covers, but not limited to, the following topics:
Supported by IEEE CIS ETTC Task Force on Intelligent Network Systems (TF-INS)
The impact of optimization in communication networks, such as Internet and mobile wireless networks,
on the modern economy and society has been growing steadily. At the present, new technologies like
5G cellular mobile radio systems, optical Internet, and network virtualization and automation are in
widespread use, allowing fast data communications, new services and applications. With the advent of
computer systems, computational intelligence approaches have been developed for systematic design,
optimization, and improvement of different communication network systems.
The aim of the special session is to promote research and reflect the most recent advances of evolutionary computation, including evolutionary algorithms, deep learning, neural network, fuzzy systems, metaheuristic techniques and other intelligent methods, in the solution of problems in communication networks. Topics of interest include, but are not limited to:
telecommunications; mobile, satellite, and optical communications; switching and routing; network functions virtualization (NFV)and software-defined networking (SDN); communication systems simulation; station and antenna design; frequency and channel assignment; information and speech processing; intrusion detection; error control coding; compression and cryptography; propagation and channel modeling, protocol design, etc.
parallel and distributed systems; networks and graph problems; unconstrained and constrained network design problems; structural and computational complexity; adaptability to environmental variations; robustness to network changes and failures; effectiveness and scalability of performance; location and link design; reliability and failure; location placement; network physical and software architecture; network hardware and software technologies; operations, maintenance, and management; signaling and control; active networks; network services and applications, etc.
Nonlinear equation systems (NESs) frequently arise in many physical, electronic, and mechanical
processes. Very often, a NES may contain multiple roots. Since all these roots are important for a
given NES in the real-world applications, it is desirable to simultaneously locate them in a single
run, such that the decision maker can select one final root which matches at most his/her
preference. For solving NESs, several classical methods, such as Newton-type methods, have been
proposed. However, these methods have some disadvantages in the sense that they are heavily
dependent on the starting point of the iterative process, can easily get trapped in a local optimal
solution, and require derivative information. Moreover, these methods tend to locate just one root
rather than multiple roots when solving NESs.
Solving NESs by EAs is a very important area in the community of evolutionary computation, which is challenging and of practical interest. However, systematic work in this area is still very limited. The aim of special session is to facilitate the development of EAs for locating multiple roots of NESs. Topics include:
Bilevel programming problems (BLPPs) are non-convex optimization problems with two levels, namely
upper level and lower level. For such hierarchical structure, we need to fix an upper decision
variable as a parameter and to solve the lower optimization problem. Such requirements make the
bilevel optimization problems difficult to solve and time consuming. The majority of existing work
on BLPPs is concentrated on linear BLPPs and some special nonlinear BLPPs in which all of the
functions involved are convex and twice differentiable. In the context of multi-objective bilevel
optimization problems, there does not exist too many studies. Most of the existing algorithms focus
on approximate solution strategies and K-K-T conditions. However, these algorithms are very time
consuming and strongly problem dependent. In order to handle such problems efficiently and
effectively, there is a need for theoretical as well as methodology advancements to solve single
bilevel and multi-objective bilevel optimization problems.
This special session on will bring together researchers working on the following topics:
This special session solicits original research papers or reviews that would shape and advance
design, manufacture and engineering management in the Industry 4.0 era. Computational intelligence
(CI) utilises a set of nature-inspired modelling and optimisation approaches to complex real-world
problems. Papers addressing how to create designs and build machines smartly, thereby leading to a
step improvement in manufacturing autonomy and industrial efficiency, performance and
competitiveness, would be particularly welcome.
Topics include, but are not limited to:
Gene Expression Programming (GEP) is a popular evolutionary algorithm for automatic generation of
computer programs. Over the past decades, GEP has undergone rapid advancements and developments. A
number of enhanced GEPs have been proposed to date, such as Parallel GEP, Cooperative
Co-evolutionary GEP, and Multi-task GEP. The real-world applications of GEP are also multiplying
fast, including regression, classification, combinatorial optimization, data mining and knowledge
The aim of this special session is to provide a forum for researchers in this field to exchange the latest advances in theories, technologies, and practice of Gene Expression Programming. Topics of interest include, but are not limited to, GEP in the following aspects:
Estimation of Distribution Algorithm (EDA) is a special kind of evolutionary algorithm that works by
constructing a probability model to estimate the distribution of the predominant individuals in the
population. In a border sense, there are also some other evolutionary computation (EC) or swarm
intelligence (SI) algorithms that work by implicitly constructing a probability distribution in the
solution space. For example, in ant colony optimization (ACO), ants deposit pheromone on paths,
which can be seen as an implicit probability model. Such implicit probability distribution
construction behavior provides a more feasible way to build promising probability distribution
models, and thus further extends the concept of Probability Distribution Based Evolutionary
Algorithms. There are a lot of applications in probability distribution based evolutionary
algorithms, for example, task scheduling, routing, mixed-variable optimization, etc. Constructing
suitable probability distribution models and studying the theory behind the probability distribution
are important for the research of probability distribution based evolutionary algorithms, which are
promising in solving such real-world applications.
The aim of this special session is to promote the research on theories and applications in this filed. Topics of interest include, but are not limited to:
This session aims to bring together both theoretical developments and applications of Computational
Intelligence to software engineering (SE), i.e. management, design, the development, operation,
maintenance, and testing of software. All bio-inspired computational paradigms and machine learning
techniques are welcome, such as genetic and evolutionary computation, including multi-objective
approaches, fuzzy logic, intelligent agent systems, neural networks, cellular automata, artificial
immune systems, swarm intelligence, and others, including machine learning techniques.
This special session aims to provide a forum for the presentation of the latest data, results, and future research directions on evolutionary methods and machine learning in software engineering. The special session invites submissions in any of the following areas:
Original research papers are solicited in related areas of biologically-inspired algorithm based
evolutionary computation for robotics. Submissions to the special session should be focused on
theoretical results or innovative applications of biologically-inspired algorithms associated with
evolutionary computation for robot and vehicle systems.
Specific topics for the special session include but are not limited to:
Swarm Intelligence (SI) algorithms consist of a population of semi-autonomous agents coupled with a
social interaction mechanism. Despite the characteristically-simple rules governing each individual
agent, an intelligent collective behaviour emerges as a result of the social interactions among
agents. Often, the emergence of such collective intelligence is contingent upon employing an
appropriate configuration of the algorithm. However, the optimal configuration(s) for the algorithms
are typically problem dependent and may change throughout the search process. Thus, determining an
appropriate configuration a priori may lead to sub-optimal performance. To address the shortcomings
of a priori configuration, adaptive SI algorithms aim to modify their configurations during the
search process based on various observations.
The purpose of this special session is to provide a forum for researchers to disseminate their original research in the field of adaptive swarm intelligence algorithms. Topics of interest include, but are not limited to, adaptive swarm intelligence algorithms in the following aspects:
Extensive research has been developed in computational intelligence and evolutionary computing,
ranging from theoretical foundations, principles, to practical applications across various domains
including medical, industry and education. It has been widely recognized that the use of Sensor
technologies and UAV Platforms is increasing among researchers and developers. It is required that
sensors can perform a rapid assessment and analysis of collected data to provide real time feedback
to the end users. UAVs are required to perform autonomous paths optimization for different research
purposes. Researchers are exploring potential novel evolutionary computation solutions for real time
The proposed session aims at demonstrating the latest research and development on evolutionary computation and their applications in sensors development and unmanned aerial vehicle UAV platform optimization. List of main topics include but not limited to:
Although Evolutionary Algorithms are very good at mimicking adaptation within a species to optimize
solutions for difficult problems, creating algorithms that can mimic the development of two or more
species from a common ancestor has been a challenge. There are versions of Evolutionary Algorithms
that have some characteristics of speciation, but none that match natural processes. Such algorithms
would be a good step in the development of a general purpose Evolutionary Algorithm and would help
in understanding the principles of evolution. In regards to this research, we consider a population
to be distinct (and a separate species) if it is made up of individuals that are unable to produce
viable offspring with individuals from the other population or if offspring are produced, they are
sterile. The short term goal, which is reasonable for this special session, is to have individuals
of differing species choose not to mate and if they do produce offspring, the offspring do not
continue to reproduce. In this way, the gene pools for each of the species will be isolated.
The purpose of this special session is to bring together people working on Evolutionary Algorithms that tend toward or have the potential for speciation. Some possible topics of interest include:
Of late, scientists have stressed upon the hybrid metaheuristics, which being a judicious combination
of several other metaheuristics, algorithms from mathematical programming, constraint programming,
or machine learning algorithms, have been found to be more robust and failsafe. The advent of the
quantum computing paradigm has also given an impetus to evolving time efficient hybrid
metaheuristics, where the conjoined principles of quantum mechanics successfully enhance the real
time performance of the hybrid metaheuristics. Clustering or cluster analysis partitions a dataset
into a meaningful group of similar objects. However, the existing methods require an a priori
knowledge about the number of clusters present in the dataset. Automatic clustering, on the other
hand, aims to find out the optimal number of clusters from a dataset without having any prior
knowledge about the number of clusters.
This special session aims to bring together recent advances in methodological approaches and applied techniques related to the use of hybrid metaheuristics for automatic clustering of data and its analysis. We are soliciting contributions on (but not limited to) the following:
The aim of this special session is designing a multi-disciplinary program for automated design of new
materials requiring knowledge of physical chemistry, evolutionary optimization (grammatical
evolution), Python programming, 3D graphics and mathematics for calculating fitness functions and 3D
positions and energies of individual atoms. The prediction of new nanostructures requires knowledge
of physical chemistry and the ability to select a suitable method of evolutionary optimization. The
first such predictor was designed by Organov (USPEX), which combines the knowledge of quantum
physics and evolutionary optimization. Since quantum physics does not contain a structural
description of atoms, this predictor is capable of designing structures with hundreds of atoms on
supercomputers. The combination of the structural description of atomic nuclei and grammatical
evolution does not have this limitation.
The topics of this special session include:
Crowdsourcing refers to the practice of involving a crowd or group of people for accomplishing some
large-scale task in an efficient or innovative way. Due to involvement of malicious crowd workers,
it is sometimes difficult to utilize the crowdsourced opinions in decision making. Different
evaluation criteria such as cost, time and accuracy are to be optimized to build an effective
crowdsourcing framework. Therefore, evolutionary and other metaheuristic optimization techniques can
be used in solving these complex problems. Moreover, machine learning techniques like deep neural
networks, support vector machines, random forest, Bayesian learning, Markov chains, and
probabilistic graphical models can be employed in different problems like aggregating crowd
opinions, classifying crowd workers, performing fusion of crowdsourced solutions, etc. Thus
combination of evolutionary algorithms and machine learning methods can solve various crowdsourcing
problems in different domains, including recommender systems, social networks, education, e-commerce
This special session aims to bring together researchers from both academia and industry to share the ideas of the application of machine learning techniques and evolutionary computation in real-life problems that employ crowdsourcing. The potential topics of interest include, but not limited to:
Evolutionary Computation (EC) is a huge and expanding field, attracting more and more interests from
both academia and industry. For the discrete domain and application scenarios, we want to pick the
best algorithms. Actually, we want to do more, we want to improve upon the best algorithm. This
requires a deep understanding of the problem at hand, the performance of the algorithms we have for
that problem, the features that make instances of the problem hard for these algorithms, and the
parameter settings for which the algorithms perform the best. Benchmarking is the engine driving
research in the fields of EAs for decades, while its potential has not been fully explored.
The goal of this special session is to solicit original works on the research in benchmarking: Works which contribute to the domain of benchmarking of discrete algorithms from the field of Evolutionary Computation, by adding new theoretical or practical knowledge. Papers which only apply benchmarking are not in the scope of the special session.
This special session wants to bring together experts on benchmarking, evolutionary computation algorithms, and discrete optimization.
Research on single objective optimization algorithms often forms the foundation for more complex scenarios, such as niching algorithms and both multi-objective and constrained optimization algorithms. Traditionally, single objective benchmark problems are also the first test for new evolutionary and swarm algorithms. Additionally, single objective benchmark problems can be transformed into dynamic, niching composition, computationally expensive and many other classes of problems. It is with the goal of better understanding the behavior of swarm and evolutionary algorithms as single objective optimizers that we are introducing the 100-Digit Challenge. The SIAM 100-Digit Challenge was developed in 2002 by Nick Trefethen in conjunction with the Society for Industrial and Applied Mathematics (SIAM) as a test for high-accuracy computing. Specifically, the challenge was to solve 10 hard problems to 10 digits of accuracy. One point was awarded for each correct digit, making the maximum score 100, hence the name. Contestants were allowed to apply any method to any problem and take as long as needed to solve it. Out of the 94 teams that entered, 20 scored 100 points and 5 others scored 99. In a similar vein, we propose the 100-Digit Challenge. In contrast to the SIAM version, this 100-Digit Challenge asks contestants to solve all ten problems with one algorithm, although limited control parameter “tuning” for each function will be permitted to restore some of the original contest’s flexibility. Another difference is that the score for a given function is the average number of correct digits in the best 25 out of 50 trials. All population-based methods are acceptable.