Provisionally Accepted IEEE CEC 2019 Special Sessions


CEC-01 Special Session on Memetic Computing
CEC-02 Special Session on Data-Driven Optimization of Computationally Expensive Problems
CEC-03 Special Session on Evolutionary Computation for Automated Algorithm Design
CEC-04 Special Session on Games
CEC-05 Special Session on Evolutionary Computation for Music, Art, and Creativity
CEC-06 Special Session on Multimodal Multiobjective Optimization
CEC-07 Special Session on Brain Storm Optimization Algorithms
CEC-08 Special Session on Evolutionary Computation and Neural Network for Combating Cybercrime
CEC-09 Special Session on Evolutionary Computation in Healthcare and Biomedical Data
CEC-10 Special Session on Pigeon Inspired Optimization
CEC-11 Special Session on Evolutionary Scheduling and Combinatorial Optimisation
CEC-12 Special Session on Evolutionary Many-objective Optimization
CEC-13 Special Session on Evolutionary Computation in Healthcare Industry
CEC-14 Special Session on Evolutionary Computation for Service and Cloud Computing
CEC-15 Special Session on New Directions in Evolutionary Machine Learning
CEC-16 Special Session on When Evolutionary Computation Meets Data Mining
CEC-17 Special Session on Evolutionary Computation for Smart Grid and Sustainable Energy Systems
CEC-18 Special Session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction
CEC-19 Special Session on Transfer Learning in Evolutionary Computation
CEC-20 Special Session on Nature-Inspired Constrained Optimization
CEC-21 Special Session on Theory of Bio-Inspired Computation
CEC-22 Special Session on Optimization, Learning, and Decision-Making in Bioinformatics and Bioengineering
CEC-23 Special Session on Evolutionary Computation in Internet of Everything
CEC-24 Special Session on Evolutionary Computation for Symbolic Modelling
CEC-25 Special Session on Hybrid Algorithms in Scheduling and Network Design
CEC-26 Special Session on Evolutionary Computation for Handling Missing Values in Data Mining
CEC-27 Special Session on Complex Networks and Evolutionary Computation
CEC-28 Special Session on Evolutionary Computation in Dynamic and Uncertain Environments
CEC-29 Special Session on Artificial Immune Systems
CEC-30 Special Session on Ethics and Social Implications of Computational Intelligence
CEC-31 Special Session on Evolutionary Computation for Smart City
CEC-32 Special Session on Cooperative Evolutionary Computation
CEC-33 Special Session on Evolutionary Computation for Finance and Economics
CEC-34 Special Session on Distributed and Advanced Evolutionary Computation in the Smart City Era
CEC-35 Special Session on Evolutionary Computation for Granular Computing
CEC-36 Special Session on Evolutionary Deep Learning and Applications
CEC-37 Special Session on Dynamic Multi-objective Optimization and its Applications
CEC-38 Special Session on Self-Organizing Migrating Algorithm



CEC-01 Special Session on Memetic Computing


Organized by Liang Feng, Yuan Yuan, and Chuan-Kang Ting

Supported by IEEE CIS ISATC Task Force on Transfer Learning and Transfer Optimization

Scope and Topics

Memetic Computing (MC) represents a broad generic framework using the notion of meme(s) as units of 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 typically perceived as individual learning procedures, adaptive improvement procedures or local search operators that enhance the capability of population based search algorithms. More recently, novel 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 success 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:

  • Single/Multi-Objective memetic algorithms for continuous or combinatorial optimization.
  • Theoretical studies that enhance our understandings on the behaviors of memetic computing.
  • Adaptive systems and meme coordination.
  • Novel manifestations of memes for problem-solving.
  • Cognitive, brain, individual learning, and social learning inspired memetic computation
  • Self-design algorithms in memetic computing.
  • Memetic frameworks using surrogate or approximation methods
  • Memetic automaton, cognitive and brain inspired agent based memetic computing
  • Data mining and knowledge learning in memetic computation paradigm
  • Memetic computing for expensive and complex real-world problems
  • Evolutionary multi-tasking


CEC-02 Special Session on Data-Driven Optimization of Computationally Expensive Problems


Organized by Chaoli Sun, Jonathan Fieldsend, and Yew-Soon Ong

Supported by IEEE CIS ECTC Task Force on Data-Driven Evolutionary Optimization of Expensive Problems

Scope and Topics

Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function 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 heuristic 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 strategies 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 and 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 researchers 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:

  • Surrogate-assisted evolutionary optimization for computationally expensive problems
  • Adaptive sampling using machine learning and statistical techniques
  • Surrogate model management in evolutionary optimization
  • Data-driven optimization using big data and data analytics
  • Knowledge acquisition from data and reuse for evolutionary optimization
  • Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems
  • Real world applications including multi-disciplinary optimization.


CEC-03 Special Session on Evolutionary Computation for Automated Algorithm Design


Organized by Nelishia Pillay and Rong Qu

Supported by IEEE CIS ISATC Task Force on Hyper-Heuristics

Scope and Topics

Computational intelligence systems play an imperative role in solving real world complex problems in industry. These systems have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering scheduling, and amongst others. Computational intelligence systems employ one or more computational 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 derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem. This makes the development of computational systems time consuming, requiring extensive expertise, and many man hours. Consequently, there have been a number of initiatives to 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:

  • Parameter control and tuning
  • Architecture design, e.g. design of neural network and multi-agent architectures
  • Automated hybridization of intelligent techniques
  • Derivation of operators
  • Derivation of construction heuristics
  • Derivation of evaluation functions
  • Automatic system development using hyper-heuristics
  • Automatic programming
  • Auto-ML
  • Search-based software engineering
  • Neuroevolution


CEC-04 Special Session on Games


Organized by Jialin Liu and Daniel Ashlock

Supported by IEEE CIS Games Technical Committee

Scope and Topics

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 problems for developing algorithms to incredibly hard problems for testing algorithms to the limit. They are also interesting to observe, fun to play, and very attractive to students. Additionally, there is great potential for CI methods to improve the design and development of both computer games as well 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:

  • Adaptation in games
  • Automatic game testing
  • Coevolution in games
  • Comparative studies (e.g. CI versus human-designed players)
  • Dynamic difficulty in games
  • Games as test-beds for algorithms
  • Imitating human players
  • Learning to play games
  • Multi-agent and multi-strategy learning
  • Player/Opponent modelling
  • Procedural content generation
  • CI for Serious Games (e.g., games for health care, education or training)
  • Results of game-based CI and open competitions


CEC-05 Special Session on Evolutionary Computation for Music, Art, and Creativity


Organized by Chuan-Kang Ting, Francisco Fernández de Vega, and Chien-Hung Liu

Supported by IEEE CIS ETTC Task Force on Creative Intelligence

Scope and Topics

Evolutionary computation (EC) techniques, including genetic algorithm, evolution strategies, genetic programming, particle swarm optimization, ant colony optimization, differential evolution, and memetic algorithms have shown to be effective for search and optimization problems. Recently, EC and deep neural networks gained several promising results and become important tools in computational 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:

  • Generation of music, visual art, literature, architecture, and industrial design
  • Algorithmic design in creative intelligence
  • Integration of EC and (deep) neural networks for creation of music and arts
  • Application of EC to music analysis, classification/clustering, composition, variation and improvisation
  • Optimization in creativity
  • Development of hardware and software for creative systems
  • Evaluation methodologies
  • Assistance of human creativity
  • Computational aesthetics
  • Emotion response
  • Human-machine creativity


CEC-06 Special Session on Multimodal Multiobjective Optimization


Organized by Jing Liang, Boyang Qu, and Dunwei Gong

Scope and Topics

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:

  • Evolutionary algorithms for multimodal multiobjective optimization
  • Hybrid algorithms for multimodal multiobjective optimization
  • Adaptable algorithms for multimodal multiobjective optimization
  • Surrogate techniques for multimodal multiobjective optimization
  • Machine learning methods helping to solve multimodal multiobjective optimization problems
  • Memetic computing for multimodal multiobjective optimization
  • Niching techniques for multimodal multiobjective optimization
  • Parallel computing for multimodal multiobjective optimization
  • Design methods for multimodal multiobjective optimization test problems
  • Decision making in multimodal multiobjective optimization
  • Related theory analysis
  • Applications


CEC-07 Special Session on Brain Storm Optimization Algorithms


Organized by Shi Cheng, Junfeng Chen, and Yuhui Shi

Scope and Topics

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence algorithm, which is based on the collective behavior of human being, that is, the brainstorming process. There are two 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 and 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 above 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:

  • Theoretical aspects of BSO algorithms;
  • Analysis and control of BSO parameters;
  • Parallelized and distributed realizations of BSO algorithms;
  • BSO for multiple/many objective optimization;
  • BSO for constrained optimization;
  • BSO for discrete optimization;
  • BSO for large-scale optimization;
  • BSO algorithm with data mining techniques;
  • BSO in uncertain environments;
  • BSO for real-world applications.


CEC-08 Special Session on Evolutionary Computation and Neural Network for Combating Cybercrime


Organized by Manoranjan Mohanty, Ajit Narayanan, and Mukesh Prasad

Scope and Topics

The volume of cybercrime is increasing daily with increasing use of the internet for email and social media purposes. The use of neural networks for tackling cybercrime is an active area of research. 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, virus 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 underexplored.

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:

  • Malware detection using neural networks and evolutionary computation
  • Internet fraud detection and prediction using neural networks and evolutionary computation
  • Intrusion detection using neural networks and evolutionary computation
  • Digital rights management using neural networks and evolutionary computation
  • Explicit content filtering using neural networks and evolutionary computation
  • Application of convolutional neural networks for multimedia security
  • Image and video forensics using convolutional neural networks
  • Cybercrime risk due to neural networks (e.g., deepfake)
  • Digital forensics for detecting neural network-based fraud


CEC-09 Special Session on Evolutionary Computation in Healthcare and Biomedical Data


Organized by Mukesh Prasad, Paul J. Kennedy, and Manoranjan Mohanty

Scope and Topics

Biomedical data contains several challenges in data analysis, including high dimensionality, class 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 reports, signals and images, and the data are available from different sources. An interesting aspect is to integrate different data sources in the data analysis process which requires exploiting the existing 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:

  • Information fusion and knowledge transfer in biomedical and healthcare applications.
  • Data Analysis of the biomedical data including genomics.
  • Text mining for medical reports.
  • Statistical analysis and characterization of biomedical data.
  • Machine Learning Methods Applied to Medicine
  • Large Datasets and Big Data Analytics on biomedical and healthcare applications.
  • Information Retrieval of Medical Images
  • Single cell sequencing analysis
  • Medical imaging and genomics


CEC-10 Special Session on Pigeon Inspired Optimization


Organized by Haibin Duan and Yin Wang

Scope and Topics

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 of 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:

  • Analysis and control of PIO parameters
  • Parallelized and distributed realizations of PIO algorithms
  • PIO for Multi-objective optimization
  • PIO for Constrained optimization
  • PIO for Discrete optimization
  • PIO algorithm with data mining techniques
  • PIO in uncertain environments
  • Theoretical aspects of PIO algorithm
  • PIO for Real-world applications


CEC-11 Special Session on Evolutionary Scheduling and Combinatorial Optimisation


Organized by Su Nguyen, Yi Mei, and Aaron Chen

Supported by IEEE CIS ETTC

Scope and Topics

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, evolutionary based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning and evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include using 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:

  • Production scheduling
  • Timetabling
  • Vehicle routing
  • Project scheduling
  • Transport scheduling
  • Airport runway scheduling
  • Grid/cloud scheduling
  • Evolutionary scheduling with big data
  • Web service composition
  • SDN scheduling
  • 2D/3D strip packing
  • Resource allocation
  • Multi-objective scheduling
  • Complex combinatorial optimization with interdependent components
  • Automated heuristic design for combinatorial optimization
  • Dynamic combinatorial optimization
  • Innovative applications of evolutionary scheduling and combinatorial optimization


CEC-12 Special Session on Evolutionary Many-objective Optimization


Organized by Rui Wang, Ran Cheng, Miqing Li and Hisao Ishibuchi

Supported by IEEE CIS ETTC Task Force on Operations Research and Management Sciences

Scope and Topics

The field of evolutionary multi-objective optimization has developed rapidly over the last 20 years, but 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 of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, 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 as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of evolutionary many-objective optimization (EMaO) algorithms.

List of topics, but are not limited to:

  • Algorithms for EMaO, including search operators, mating selection, environmental selection and population initialization
  • Performance indicators for EMaO
  • Benchmark functions for EMaO
  • Visualization techniques for EMaO
  • Objective reduction techniques for EMaO
  • Preference articulation and decision making methods for EMaO
  • Constraint handling methods for EMaO
  • EMaO in combinatorial/discrete problems
  • EMaO in dynamic environments
  • EMaO in large-scale problems


CEC-13 Special Session on Evolutionary Computation in Healthcare Industry


Organized by Handing Wang, Rong Qu, Yaochu Jin

Supported by IEEE CIS ISATC Task Force on Intelligence Systems for Health

Scope and Topics

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. Two 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 trauma system and pharmaceutical manufacturing, other problems in the healthcare industry like decision making 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 to the healthcare community. This special session aims to promote the research on evolutionary computation methods for their application to the healthcare industry.

The topics of this special session include but are not limited to the following topics:

  • Evolutionary computation in resource allocation for hospital location planning, aeromedical retrieval system planning, etc.
  • Application of evolutionary computation for job scheduling, such as ambulance scheduling, nurse scheduling, job scheduling in medical device and pharmaceutical manufacturing, etc.
  • Multiple-criteria decision-making for computer-aided diagnosis using expert systems.
  • Web self-diagnostic system with the application of information retrieval and recommendation system.
  • Learning and optimization for vaccine selection and personalized/stratified medicine.
  • Data-driven surrogate-assisted evolutionary algorithms in pharmaceutical manufacturing processes.
  • Modeling and prediction in epidemic surveillance system for disease prevention.
  • Route planning for disability robots.


CEC-14 Special Session on Evolutionary Computation for Service and Cloud Computing


Organized by Hui Ma, Yi Mei, and Mengjie Zhang

Supported by IEEE CIS ETTC

Scope and Topics

Services computing is becoming more and more prominent in the Internet environment with the rapid growth of services available on the internet. Cloud computing has become a scalable services consumption and delivery platform in the field of Services Computing. This raises issues for service providers such as Web service composition and service location allocation, resource allocation and scheduling, etc. Computational Intelligence (CI) has been successfully applied to many challenging real-world problems. This special session aims to solve the service and cloud computing problems with CI techniques, covering all different evolutionary computation paradigms such as GA, GP, EP, ES, MA, LCS, PSO, ACO, DE, and EMO.

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 Web service composition
  • Evolutionary Web service workflow optimisation
  • Evolutionary Web service selection
  • Evolutionary Web service location allocation
  • Evolutionary Web service scheduling
  • Evolutionary semantic Web service composition
  • Evolutionary dynamic Web service composition
  • Multi-objective Web service composition
  • Evolutionary computation for resource allocation in Cloud computing
  • Evolutionary computation for workflow management in Cloud
  • Evolutionary computation for distributed Web service composition
  • Novel representations and search operators for Service-oriented computing
  • Cooperative coevolution for Service-oriented computing
  • Evolutionary computation for Big Data As A Service
  • Evolutionary computation for Internet of Things Services
  • Hybrid algorithms between EC techniques and other CI and learning techniques such as neural networks and fuzzy systems for service and cloud computing


CEC-15 Special Session on New Directions in Evolutionary Machine Learning


Organized by Masaya Nakata , Yusuke Nojima and Will Browne

Scope and Topics

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:

  • Evolutionary learning systems (e.g., learning classifier systems)
  • Evolutionary neural network (e.g., neuroevolution, evolutionary deep neural networks)
  • Evolutionary decision trees
  • Evolutionary cascade systems
  • Evolutionary fuzzy systems
  • Evolutionary reinforcement learning
  • Evolutionary ensemble systems
  • Evolutionary adaptive systems
  • Artificial immune systems
  • Genetic programming applied to machine learning
  • Evolutionary feature selection and construction for machine learning
  • Transfer learning; learning blocks of knowledge (memes, code, etc.)
  • Accuracy-interpretability tradeoff in EML
  • Applications and theory of EML


CEC-16 Special Session on When Evolutionary Computation Meets Data Mining


Organized by Zhun Fan, Xinye Cai, Chuan-Kang Ting

Scope and Topics

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 fields of 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 mining and machine learning techniques can also be used to analyze these data for improving the performance of EC.

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:

  • EC enhanced by data mining and machine learning concepts and/or framework
  • Data mining and machine learning based on EC techniques
  • Machine learning enhanced or model-based multi/many-objective optimization
  • Data mining and machine learning enhanced combinatorial, constrained, large-scale, or dynamic optimization
  • Data mining and machine learning enhanced memetic computation or local search
  • Association rule mining based on multi-objective optimization
  • Knowledge discovery in data mining via EC
  • Genetic programming in data mining
  • Multi-agent data mining using EC
  • Medical data mining with EC
  • Evolutionary clustering in noisy data sets
  • Big data projects with EC
  • Deep learning with EC
  • Real-world applications


CEC-17 Special Session on Evolutionary Computation for Smart Grid and Sustainable Energy Systems


Organized by Zhile Yang, Kunjie Yu, Zhou Wu

Scope and 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 cost and relief energy waste from power system and energy application side. However, some key problems are of 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 problem 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 scheduling 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:

  • Unit commitment, economic dispatch and optimal power flow
  • Optimal smart grid scheduling and integration with renewable energy generations
  • Energy management, intelligent coordination and control of electric vehicles/ships
  • Life cycle analysis and optimization of building energy systems
  • Charging and discharging strategies for energy storage battery systems
  • Internal and whole scale management for single and hybrid energy storage systems
  • Energy reduction strategies for food and chemical process industry
  • Energy reduction strategies for energy intensive manufacturing processes
  • Parameters identification for photovoltaic models and PEM fuel cells
  • Thermodynamic optimisation for heat exchanger design and Organic Rankine Cycle


CEC-18 Special Session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction


Organized by Bing Xue, Mengjie Zhang, and Yaochu Jin

Supported by IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction

Scope and Topics

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, covering 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:

  • Dimensionality reduction
  • Feature ranking/weighting
  • Feature subset selection
  • Multi-objective feature selection
  • Filter, wrapper, and embedded methods for feature selection
  • Feature extraction or construction
  • Filter, wrapper, and embedded methods for feature extraction
  • Multi-objective feature extraction
  • Feature selection, extraction, and dimensionality reduction in image analysis, pattern recognition, classification, clustering, regression, and other tasks
  • Feature selection, extraction, and dimensionality reduction on high-dimensional and large-scale data
  • Analysis on evolutionary feature selection, extraction, and dimensionality reduction algorithms
  • Hybridisation of evolutionary computation and neural networks, and fuzzy systems for feature selection and extraction
  • Hybridisation of evolutionary computation and machine learning, information theory, statistics, mathematical modelling, etc., for feature selection and extraction
  • Real-world applications of evolutionary feature selection and extraction, e.g. images and video sequences/analysis, face recognition, gene analysis, biomarker detection, medical data analysis, hand written digit recognition, text mining, instrument recognition, power system, financial and business data analysis, etc.


CEC-19 Special Session on Transfer Learning in Evolutionary Computation


Organized by Bing Xue, Liang Feng, Yew Soon Ong, and Mengjie Zhang

Supported by IEEE CIS ISATC Task Force on Transfer Learning & Transfer Optimization

Scope and Topics

Transfer learning aims to transfer knowledge acquired in one problem domain, i.e. the source domain, onto 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 and 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:

  • Evolutionary supervised, unsupervised, and semi-supervised transfer learning
  • Domain adaptation and domain generalization
  • Instance based transfer approaches
  • Feature based transfer learning
  • Parameter/model based transfer learning
  • Relational based transfer learning
  • Evolutionary transfer learning for classification, regression, and clustering
  • Evolutionary transfer learning for other data mining tasks, such as association rules and link analysis
  • Evolutionary transfer learning for numeric optimisation tasks
  • Evolutionary transfer learning for scheduling and combinatorial optimisation tasks
  • Hybridisation of evolutionary computation, neural networks, and fuzzy systems for transfer learning
  • Hybridisation of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning
  • Theoretical studies on the behaviours of evolutionary transfer learning and optimisation
  • Real-world applications of transfer learning in evolutionary computation, e.g. text mining, image analysis, face recognition, WiFi localisation, etc.


CEC-20 Special Session on Nature-Inspired Constrained Optimization


Organized by Efrén Mezura-Montes, Helio J.C. Barbosa, and Rituparna Datta

Supported by IEEE CIS ECTC Task Force on Nature-Inspired Constrained Optimization

Scope and Topics

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:

  • Novel constraint-handling techniques for EAs and SIAs
  • Novel constraint-handling techniques for constrained dynamic optimization
  • Novel/adapted search algorithms for constrained optimization
  • Memetic algorithms in constrained search spaces
  • Parameter setting (tuning and control) in constrained optimization
  • Mixed (discrete-continuous) constrained optimization
  • Theoretical analysis and complexity of algorithms in constrained optimization
  • Convergence analysis in constrained optimization
  • Performance evaluation of algorithms in constrained optimization
  • Expensive Constrained Optimization
  • Design of difficult and scalable test functions
  • Applications


CEC-21 Special Session on Theory of Bio-Inspired Computation


Organized by Mojgan Pourhassan, Frank Neumann, and Chao Qian

Supported by IEEE CIS Task Force on Theory of Bio-inspired Computing

Scope and Topics

The area of runtime analysis for bio-inspired computing techniques provides new insights into the working 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:

  • Theoretical foundations of bio-inspired heuristics
  • Exact and approximation runtime analysis
  • Parametrized complexity analysis
  • Black box complexity
  • Self-adaptation
  • Population diversity
  • Population dynamics
  • Fitness landscape and problem difficulty analysis
  • All problem domains will be considered including:
    • combinatorial and continuous optimization
    • single-objective and multi-objective optimization
    • constraint handling
    • dynamic and stochastic optimization
    • co-evolution and evolutionary learning


CEC-22 Special Session on Optimization, Learning, and Decision-Making in Bioinformatics and Bioengineering


Organized by Joseph A. Brown, Gonzalo Ruz, Daniel Ashlock, and Richard Allmendinger

Supported by IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering

Scope and Topics

Bioinformatics and Bioengineering (BB) are interdisciplinary scientific fields involving many branches of 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 to 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

  • (Single and multiobjective) optimization techniques for Bioinformatics and Bioengineering (BB) problems
  • Decision-making and MCDM techniques for BB problems
  • Experimental optimization of BB problems
  • Learning in/from the optimization of BB problems
  • Data-driven optimization for BB problems
  • Tuning of optimization, learning and decision-making techniques for BB problems
  • Emerging topics in BB
    • Novel applications
    • Novel challenges
    • Interactive visualization
    • Predictive fitness landscape design
    • Many-objective optimization
    • Ecoinformatics
    • Side effect machines and other kernel representations for sequence analysis
    • Biomedical data modelling and mining


CEC-23 Special Session on Evolutionary Computation in Internet of Everything


Organized by Kusum Deep, Hesham Elsayed, and Mukesh Prasad

Scope and Topics

Evolutionary Computation (EC) techniques are widely used in Internet of Everything (IoEt) applications 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 complex learning, planning, and decision-making problems in a decentralized fashion. It is able to execute large scale computation through distributed computing resources. These properties allow it to solve problems that require the processing of very large data sets. This approach, when put together with the idea of Internet of Everything, opens up a new world of applications of artificial intelligence in a localised manner.

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:

  • Data Analytics middleware for Edge computing
  • Cloud-based intelligent analytics
  • Edge-node-driven data analytics
  • Intelligent data synchronization and updating between Edge nodes or Cloud nodes
  • Data metering for Edge nodes and Cloud nodes
  • Intelligent pricing mechanisms for Edge nodes and Cloud nodes
  • Data-driven privacy and security solutions in Edge computing and Cloud computing
  • Case studies for data analytics using Edge nodes or Cloud nodes


CEC-24 Special Session on Evolutionary Computation for Symbolic Modelling


Organized by Qi Chen, Bing Xue and Mengjie Zhang

Scope and Topics

Symbolic modeling is the process of developing symbolic descriptions/mathematical models to capture the structure of the data and make an accurate prediction. In evolutionary computation, symbolic modeling 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:

  • Model evaluation and selection
  • Feature selection, feature construction and dimensionality reduction on high-dimensional regression data
  • Instance selection on large-scale regression data
  • Generalisation of symbolic models
  • Interpretability of symbolic models
  • Multi-objective symbolic modelling
  • Multivariate symbolic modelling
  • Visualisation in symbolic modelling
  • Symbolic modelling on incomplete data
  • Symbolic modelling on unbalanced data
  • Symbolic modelling on noisy data
  • Symbolic modelling on time series
  • Hybrid methods for symbolic modelling
  • Transfer learning in symbolic modelling
  • Novel applications of symbolic modelling on real-world problems in science, industry, economics, finance, etc.


CEC-25 Special Session on Hybrid Algorithms in Scheduling and Network Design


Organized by Ruibin Bai, Rong Qu, and Ben Niu

Scope and Topics

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:

  • Hybrid Algorithms for Combinatorial Optimisation Problems
  • Evolutionary Branch and Price Method
  • Machine Learning and Optimisation in Scheduling
  • Machine Learning and Optimisation in Network Design
  • Production Scheduling
  • Personnel Scheduling
  • Healthcare Scheduling
  • Vehicle Routing
  • Sports Scheduling
  • Transportation Network Optimisation
  • Communication Network Optimisation
  • Service Network Design Problems
  • Supply Chain Optimisation
  • Educational Timetabling
  • Other Scheduling Problems
  • In-depth Experimental Analysis and Comparisons Between Different Techniques
  • Meta-analytics and Search Space Landscape Analyses
  • Automated Design of Metaheuristics
  • Complexity Analyses in Scheduling
  • Complexity Analyses in Network Design Problems
  • Interactive Scheduling using Computational Intelligence
  • Experiences of CI within Scheduling
  • Case Studies in Scheduling and Network Design


CEC-26 Special Session on Evolutionary Computation for Handling Missing Values in Data Mining


Organized by Cao Truong Tran, Bing Xue and Mengjie Zhang

Scope and Topics

Many of industrial and research databases suffer from an unavoidable problem of data incompleteness. In 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, but 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:

  • Classification, regression, and time-series analysis with missing data
  • Association and clustering with missing data
  • Feature extraction, feature construction, feature selection and feature ranking/weighting with missing data
  • Evolutionary computation for imputing missing values
  • Evolutionary computation for directly evolving models with missing data
  • Ensemble learning for data mining with missing data
  • Hybridisation of evolutionary computation and machine learning for handling missing values
  • Hybridisation of evolutionary computation and fuzzy systems for handling missing values
  • Handling missing values in real-world data such as survey data, mechanical data, medical data, gene expression data, etc.


CEC-27 Special Session on Complex Networks and Evolutionary Computation


Organized by Jing Liu, Wenbo Du, and Xingyi Zhang

Scope and Topics

The application of complex networks to evolutionary computation (EC) has received considerable attention from the EC community in recent years. The most well-known study should be the attempt of using complex networks, such as small-world networks and scale-free networks, as the potential population structures in evolutionary algorithms (EAs). Structured populations have been proposed to as a means for improving 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:

  • Complex networks and fitness landscape analysis
  • Complex networks and problem difficulty prediction
  • Evolutionary dynamics on complex networks
  • Evolutionary algorithms based on complex networks
  • Community detection using evolutionary algorithms
  • Community detection using multi-objective evolutionary algorithms
  • Real world applications of evolutionary algorithms based on complex networks


CEC-28 Special Session on Evolutionary Computation in Dynamic and Uncertain Environments


Organized by Changhe Li, Michalis Mavrovouniotis, and Shengxiang Yang

Supported by IEEE CIS TF on Evolutionary Computation in Dynamic and Uncertain Environments

Scope and Topics

Many real-world optimization problems are subject to dynamism and uncertainties that are often impossible 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 place of the computationally expensive high-fidelity fitness function). In addition, the design variables or environmental conditions can be perturbed, or they change over time. The tools to solve these dynamic and uncertain optimization problems (DOP) should be flexible, able to tolerate uncertainties, fast to allow reaction to changes and adaptation. Moreover, the objective of such tools is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to 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:

  • Benchmark problems and performance measures
  • Dynamic single - and multi-objective optimization
  • Dynamic constrained optimization
  • Adaptation, learning, and anticipation
  • Models of uncertainty and their management
  • Handling noisy fitness functions
  • Using fitness approximations
  • Searching for robust optimal solutions
  • Algorithm comparison and benchmarking
  • Hybrid approaches
  • Theoretical analysis
  • Real-world benchmarks and applications


CEC-29 Special Session on Artificial Immune Systems


Organized by Zaineb Chelly Dagdia, Wenjian Luo, and Yong Wang

Supported by IEEE CIS ETTC Task Force on Artificial Immune Systems

Scope and Topics

The biological immune system is a powerful defense system that protects an organism itself from diseases 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 branch in the computational intelligence community, which is inspired by the structure, functions, models and 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 simulation of the biological immune system, to the development of novel engineering solutions to complex problems.

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:

  • Computational and mathematical modelling of the immune system
  • Novel immune algorithms and new immune operators
  • Theoretical aspects of immune inspired algorithms
  • Empirical investigations on immune inspired algorithms
  • Benchmarking immune inspired algorithms against other techniques
  • Hybridization of immune inspired algorithms with other techniques
  • Systems and synthetic immunology


CEC-30 Special Session on Ethics and Social Implications of Computational Intelligence


Organized by Matt Garratt, Keeley Crocket, and Bob Reynolds

Supported by IEEE CIS Task Force on the Ethical and Social Implications of Computational Intelligence

Scope and Topics

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? Moreover, is the current legal framework capable of dealing with the repercussions of decisions made by CI systems on matters such as finance, medical treatments or autonomous vehicle collision avoidance. This special session aims to discuss solutions to some of these challenges, what safeguards might be required (both technologically and legally) and how we can better present the benefits of CI to the wider community.

Topics of interest include, but are not limited to:

  • Potential effects of CI on the human workforce and distribution of wealth
  • Potential effects of CI on privacy
  • Possible bias in CI systems (e.g. can a deep neural network trained to detect lying from spoken language be more likely to get a false positive results for one racial group more than another)
  • Safety of CI systems embedded in autonomous and automated systems (e.g. autonomous vehicles, nuclear power plant control systems)
  • Human-machine Trust in CI Systems
  • Specific applications of CI and the potential ethical/social benefits and risks (e.g. Marking of student assignments, assessment of legal documents, automated decision making in the stock market, medical research)
  • Legal implications of CI (e.g. legal liabilities when things go wrong; how do you certify systems that can ‘learn’ from their environment etc)
  • Need and direction for developing formal standards in ethics for CI
  • Public perception of CI


CEC-31 Special Session on Evolutionary Computation for Smart City


Organized by Kaizhou Gao, Jing Liang, and Ling Wang

Scope and Topics

A Smart City is an urban area that uses different types of electronic data collection sensors to supply 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 of things or IoT) to optimize the efficiency of city operations and services. A smart city is powered by “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:

  • Traffic signals control and optimization
  • Intelligent parking spot allocation
  • Public transport route planning
  • Air-conditioning energy optimization in smart building
  • Urban water supply system optimization
  • Urban gas supply system optimization
  • Evolutionary computation for emergency evacuation of the crowd
  • Evolutionary computation for internet of things
  • Evolutionary computation for public vehicle routing and planning
  • Other topics in smart city


CEC-32 Special Session on Cooperative Evolutionary Computation


Organized by Mohammed El-Abd, Junfeng Chen, and Shi Cheng

Scope and Topics

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 classified as implicit or explicit. In implicit decomposition, multiple instances implicitly tackle different areas 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 evolutionary algorithm) or heterogeneous (instances of different evolutionary algorithms). Many cooperative search 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:

  • Theoretical analysis (mathematical modeling, stability, convergence … etc.) of cooperative evolutionary algorithms
  • Control, parameter tuning, and adaptation strategies of cooperative evolutionary algorithms
  • Parallelized/Hardware implementations (clusters, GPUs, FPGAs … etc.) of cooperative evolutionary algorithms
  • Novel cooperative techniques (frameworks, problem decomposition, information exchange, etc.)
  • Hybrid cooperative evolutionary algorithms
  • Different types of optimization problems: constrained and unconstrained, single, multi and many-objective, continuous and discrete optimization, mixed decision variables, dynamic optimization, and large-scale optimization
  • Real-world applications of cooperative evolutionary algorithms


CEC-33 Special Session on Evolutionary Computation for Finance and Economics


Organized by Michael Kampouridis and Fernando Otero

Supported by IEEE CIS Computational Finance and Economics TC

Scope and Topics

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):

  • Algorithmic trading
  • Artificial stock markets
  • Agent-based models
  • Digital currencies
  • Financial forecasting
  • Financial engineering
  • Financial networks
  • Insurance
  • Portfolio selection and management
  • Pricing complex financial products
  • Risk management systems


CEC-34 Special Session on Distributed and Advanced Evolutionary Computation in the Smart City Era


Organized by Yuji Sato, Noriyuki Fujimoto, and Toshimichi Saito

Scope and Topics

The smart city concept integrates information and communication technology, and various physical devices connected to the cloud network to optimize the efficiency of city operations and services and connect to 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 cores 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:

  • Implementation of massively parallel EC in cloud computing systems and/or services
  • Parallel and distributed evolutionary algorithms in the smart city
  • Distributed multi-objective swarm intelligence in the smart city
  • Evolutionary learning for multimodal interface problems
  • Neuro-evolution for multi-task problems in the smart city
  • Distributed multi/many objective evolutionary algorithms involving several conflicting objectives
  • Applications of parallel and EC techniques in the smart city Assistance of human creativity
  • Applications of EC and other bio-inspired paradigms to peer to peer systems, and distributed EC algorithms that use them.


CEC-35 Special Session on Evolutionary Computation for Granular Computing


Organized by Weiping Ding and Gary G. Yen

Supported by IEEE CIS FSTC Task Force on Adaptive and Evolving Fuzzy Systems

Scope and Topics

Granular computing (GC) focuses on the knowledge representation and reasoning with information granules, 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 applying evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are robust and adaptive search methods, which can perform a global search in the space of candidate solutions. The benefits of exploring the combination of granular computing and evolutionary computation will have an impact in multiple research disciplines and industry domains, including transportation, communications, 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:

  • Fuzzy sets method and system with evolutionary algorithm
  • Rough sets method and system with evolutionary algorithm
  • Multi-objective evolutionary algorithm for granular computing
  • Evolutionary fuzzy deep neural network for data classification
  • Evolutionary multimodal optimization for fuzzy system
  • Quantum-inspired evolutionary algorithm for granular computing
  • Co-evolutionary algorithm for granular computing framework
  • Granular data mining for feature learning, classification, regression, and clustering with evolutionary algorithm
  • Granular data mining for multi-task modeling, multi-view modeling and co-learning with evolutionary algorithm
  • Real-world applications using evolutionary granular computing


CEC-36 Special Session on Evolutionary Deep Learning and Applications


Organized by Yanan Sun, Bing Xue, Mengjie Zhang, and Chuan-Kang Ting

Supported by

  • IEEE CIS ISATC Task Force on Evolutionary Deep Learning and Applications
  • IEEE CIS ETTC Task Force on Evolutionary Computation for Feature Selection and Construction
  • IEEE CIS ETTC Task Force on Evolutionary Computer Vision and Image Processing

Scope and 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 structure requires rich domain knowledge on both the investigated data and the general data analysis domain, which 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.

  • Representation methods for huge number of parameters
  • Representation methods for variable-length individuals
  • Global and/or local search operators for variable-length individuals
  • New search operators for evolutionary deep learning
  • Large-scale optimization algorithms for deep learning
  • Fast fitness evaluation algorithms in evolving deep learning
  • Multi- and many-objective optimization in evolving deep learning
  • Hybrid methods for evolutionary deep learning
  • Evolutionary deep learning for supervised learning
  • Evolutionary deep learning for unsupervised learning
  • Evolutionary deep learning for reinforcement learning
  • Evolutionary computation for optimizing the structure of the deep neural networks
  • Real-world applications of evolutionary deep learning


CEC-37 Special Session on Dynamic Multi-objective Optimization and its Applications


Organized by Daming Shi, Marde Helbig, and Maysam Orouskhani

Scope and Topics

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 are called: Dynamic multi-objective optimization (DMOO) problems. Here, where either the objective functions 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:

  • Complexity, theoretical analysis, and convergence criterion of DMOO
  • Constraint and noise handling methods for DMOO
  • Benchmarks and performance measures for DMOO
  • Evolutionary dynamic multi-objective optimization
  • Dynamic many-objective optimization
  • Dynamic multi-objective Deep Learning techniques
  • Fuzzy dynamic multi-objective optimization
  • Applications: bio-medical date modeling, Big data, and …


CEC-38 Special Session on Self-Organizing Migrating Algorithm


Organized by Ivan Zelinka, Swagatam Das, and Ponnuthurai Nagaratnam Suganthan

Scope and Topics

This special session is concerned about the Self-Organizing Migrating Algorithm (SOMA), belonging to the class of swarm intelligence techniques. Compare to other prominent swarm intelligence paradigms, as for example Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly, and so on, SOMA is distinguished by competitive-cooperative phases, inherent self-adaptation of movement over the search 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 reactor control, aircraft wings optimisation, chaos control, large scale, combinatorial and permutative optimisation tasks.

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:

  • Theoretical aspects of SOMA
  • SOMA hybridisation with other metaheuristics
  • Performance improvement, testing and efficiency of SOMA
  • SOMA for complex optimisation scenarios
  • SOMA and its parallelisation
  • SOMA for discrete optimisation
  • Randomness, chaos and its impact on SOMA dynamics and algorithm performance
  • SOMA in real-world applications