The 11th International Conference on Simulated Evolution and Learning
November 10-13, 2017, Shenzhen, China

Call for Special Session Proposals

Accepted Special Sessions

  • Special Session 1:Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction
  • Special Session 2:Swarm Intelligence Algorithms, Simulation, Theories and Applications
  • Special Session 3:Swarm and Evolutionary Computation in Dynamic and Uncertain Environments
  • Special Session 4:Evolutionary Optimization for Operations Research

Special Session 1

Title: Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction
This special session continues the previous one in SEAL 2014, focusing mainly on Evolutionary Computation for Feature selection, feature extraction or construction and dimensionality reduction, to improve the feature space quality in learning tasks. This is one of the key areas in the SEAL conference. In machine learning and data mining, the quality of the input data determines the quality of the output (e.g. accuracy), known as the GIGO (Garbage In, Garbage Out) principle. For a given problem, the input data of a learning algorithm is almost always expressed by a number of features (attributes or variables). Therefore, the quality of the feature space is a key for success of any machine learning and data algorithm.
Feature selection, feature extraction or construction and dimensionality reduction are important and necessary data pre-processing steps to increase the quality of the feature space, especially with the trend of big data. Feature selection aims to select a small subset of important (relevant) features from the original full feature set. Feature extraction or construction aims to extract or create a set of effective features from the raw data or create a small number of (more effective) high-level features from (a large number of) low-level features. Dimensionality reduction aims to reduce the dimensionality of the data space with the focus of solving “the curse of dimensionality” issue. All of them can potentially improve the performance of a learning algorithm significantly in terms of the accuracy, increase the learning speed, and the complexity and the interpretability of the learnt models. However, they are challenging tasks due to the large search space and feature interaction problems. Recently, there has been increasing interest in using evolutionary computation techniques to solve these tasks due to the fast development of evolutionary computation and capability of stochastic search, constraint handling and dealing with multiple conflict objectives.
The theme of this special session is the use of evolutionary computation for feature reduction, covering ALL different evolutionary computation paradigms. The aim is to investigate both the new theories and methods in different evolutionary computation paradigms to feature selection, feature extraction and construction, dimensionality reduction and related studies on improving quality of the feature space, and their applications. 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
  • Single feature or multiple features 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 classification, diagnosis, and analysis, hand written digit recognition, text mining, instrument recognition, power system, financial and business data analysis, et al.
Dr Bing Xue 
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand

Prof. Mengjie Zhang
School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand

Special Session 2

Title: Swarm Intelligence Algorithms, Simulation, Theories and Applications
Swarm intelligence refers to the problem-solving capability by taking inspiration from the collective activities of social organisms, such as like the birds, fishes, ants, bees, and bacteria. The basic operators, the life-cycle principles, the interactions between the simple-information-processing colonies, and the unique exploration and exploitation strategies can widen the insights of humans to manage the complex systems from distinct aspects. The typical swarm intelligence algorithms include Particle Swarm Optimization, Ant Colony Optimization, Bacterial Foraging Optimization, and Artificial Bee Colony Optimization, etc. The applications of those optimization algorithms are fairly vast, such as parameter optimization, job scheduling, design optimization, data mining, and pattern recognition.
The aim of this special session is to collect a series of latest advantages and contributions in theories, technologies, and simulations. Applications of those swarm intelligence algorithms are all welcome. Research topics related to this special issue include, but are not limited to, the following topics:
  • Particle swarm optimization
  • Bacterial colony optimization
  • Artificial bee colony optimization
  • Bacterial foraging optimization
  • Artificial fish search algorithm
  • Fireworks algorithm
  • Brain storm optimization algorithm
  • Swarm based multi-objective optimization algorithms
  • Other swarm based algorithms
Applications of the above algorithms include but not limited to
  • Large scale optimization problems
  • Optimization in dynamic and uncertain environment
  • Operations research
  • Planning and operations in industrial systems, transportation systems, and other systems
  • Decision making
  • Management optimization
  • Finance and economics
  • Games
  • Machine learning
  • Data mining and data clustering
  • Image processing
  • Feature selection
  • Information security
  • Power and energy systems
  • Bioengineering
  • Swarm robotics, and Other relating applications
Prof Ben Niu
Shenzhen University, Shenzhen, China 
Email: drniuben@gmail.comd
Prof Jing Liang
Zhenzhou University, Zhenzhou, China
Dr Bing Xueg
Victoria University of Wellington (VUW), New Zealand
Dr Hong Wang
The Hong Kong Polytechnic University, Hong Kong, China

Special Session 3

Title: Swarm and Evolutionary Computation in Dynamic and Uncertain Environments
Many real-world optimization problems have characteristics of uncertainties. These uncertainties include dynamics, noise, approximations, and robustness. Traditional optimization methods often fail to solve these kinds of problems. Swarm and evolutionary computation methodologies, inspired by natural processes or creatures, have shown success in these kinds of optimization problems. The aim of SECDUE is to provide an opportunity for students, researchers, and practitioners to meet and to present and discuss the state-of-the-arts as well as the future directions in this domain. Any contribution, both empirical and theoretical, related to swarm and evolutionary computation metaheuristics in dynamic and uncertain environments is accepted.
Topics of interest include, but are not limited to, any of the followings:
  • benchmark problems and performance measures
  • dynamic single-objective optimization
  • dynamic multi-objective optimization
  • dynamic constrained optimization
  • handling noisy fitness functions
  • fitness approximations / surrogate-assisted optimization
  • robust solutions and robust optimization
  • data-driven optimization in uncertain environments
  • dynamic and robust optimization benchmark problems
  • optimization in games and related domains
  • knowledge-based evolutionary optimization in uncertain environments
  • swarm-based algorithms in dynamic and uncertain environments
  • hybrid approaches
  • theoretical analysis
  • real-world applications
Dr Changhe Li
China University of Geosciences,Wuhan, China
Dr Trung Thanh Nguyen
Liverpool John Moores University, Liverpool, United Kingdom
Dr Michalis Mavrovouniotis
Nottingham Trent University, Nottingham, United Kingdom
Prof Shengxiang Yang
De Montfort University, Leicester, United Kingdom

Program committee members:
  • Yew-Soon Ong, Nanyang Technological University, Singapore
  • Aimin Zhou, East China Normal University, China
  • Juergen Branke, University of Warwick, UK
  • David Pelta, University of Granada, Spain
  • Ke Tang,University of Science and Technology of China, China
  • Ponnuthurai Nagaratnam Suganthan, Nanyang Technological University, Singapore
  • Xiaodong Li, RMIT University, Australia
  • Hui Wang, Nanchang Institute of Technology, China
  • Miqing Li, University of Birmingham, UK
  • Yaochu Jin, University of Surrey, UK
  • Maoguo Gong, Xidian University, China
  • Yinan Guo, China University of Mining and Technology, China
  • Ming Yang, China University of Geosciences, China
  • Ran Cheng, University of Birmingham, UK
  • Andries P. Engelbrecht, University of Pretoria, South Africa
  • More to come

Special Session 4

Title: Evolutionary Optimization for Operations Research
Computational methods and operations research (OR) have been linked since their origin. In recent years, a number of powerful evolutionary optimization methods have been proposed to find satisfactory solutions to a variety of operational problems, for example, production planning and scheduling, supply chain management, transportation, and disaster relief. The increasing complexity of today’s OR problems has also presented great challenges for the research and development of evolutionary algorithms (EAs). Since different algorithms have different performances to different problems, it is of crucial importance to study how to adapt existing EAs and design new EAs for special classes of OR problem.
The purpose of this special session is to present the recent advances on evolutionary optimization methods for OR problems. Original contributions in, but not limited to, the following topics are enthusiastically invited:
  • Adaption of existing EAs for optimization problems in OR.
  • Specially designed EAs for optimization problems in OR.
  • Theoretical analysis of evolutionary mechanisms on special OR problems.
  • Experimental comparison of different EAs on benchmark OR problems.
  • Real-world applications in the private and public sectors.
Yu-Jun Zheng received his PhD degree from Institute of Software, Chinese Academy of Science in 2010, and now is an associated professor and a PhD supervisor in Zhejiang University of Technology, China. He is an IEEE member and an ACM member, and his research interests include intelligent computing and its application in operations research. He has authored over 60 scientific papers in well-known international journals and conferences, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Intelligent Systems, etc. In 2014, he received the runner-up of IFORS Prize for OR Development due to the work of biogeography-based optimization for emergency engineering rescue scheduling in disaster relief operations in China.
Xin-Chao Zhao received his PhD degree from Academy of Mathematics and Systems Science, Chinese Academy of Science in 2005, and now is a professor and a PhD supervisor in Beijing University of Posts and Telecommunications University, China. From 2013 to 2014 he was invited as a visiting professor in Essex University and Birmingham University, UK. His main research interests include swarm intelligence, evolutionary computation, and operations research.

Program committee members:
  • Yu-Jiao Huang, Zhejiang University of Technology, China,
  • Hai-Feng Ling, PLA University of Science & Technology, China,
  • Wei-Guo Sheng, Hangzhou Normal University, China,
  • Yu-Jun Zheng, Zhejiang University of Technology, China,
  • Xin-Chao Zhao, Beijing University of Posts and Telecommunications, China,

Special Session Paper Submission

Prospective special session authors are invited to submit their papers through the SEAL 2017 conference paper submission site at:
Please select/specify the special session that you are submitting your paper to.

About Special Sessions

SEAL 2017 strongly encourages the organization of Special Sessions to supplement regular programs, aiming to provide a forum for focused discussions on new learning paradigms, efficient optimization methods, and innovative applications of established approaches to real-life problems.

All Proposals Must Include

  • A topical title of the proposed session.
  • The name(s), email(s), and brief bio(s) of the organizer(s).
  • The motivation of the proposed session, stating its relevance with SEAL (less than 200 words).
  • A short description of the main scopes of the proposed session to be covered (less than 200 words).
  • A list of the special session program committee members if available
Please provide all the information requested above when submitting your Special Session proposal. Each proposal must be emailed in PDF format to the Special Sessions co-chairs, indicating “[SEAL2017] Special Session Proposal” in the subject line.

Review Criteria

Special session proposals will be reviewed by the SEAL 2017 Special Session Chairs. Each paper submitted to Special Sessions will be peer-reviewed by at least three SEAL 2017 Program Committee members and/or the corresponding special session program committee members along the same acceptance criteria as the Regular Sessions. All accepted papers will be published in the SEAL 2017 Proceedings as a volume of the series Lecture Notes in Computer Science (LNCS) by Springer.

Important Dates

  • Proposals Submission Deadline for Special Sessions April 15, 2017
  • Final Notification of Proposals Acceptance April 22, 2017
  • Papers Submission Deadline for Special Sessions May 20, 2017
  • Final Notification of Papers Acceptance Jul. 15, 2017
  • Submission of Camera-Ready Papers Aug. 15, 2017
Please direct questions or suggestions to the Special Session co-chairs:
Ben Niu
Cara Macnish