IEEE CEC 2019 Special Sessions





CEC-01 Special Session on Memetic Computing


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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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