Proposals for the organization of IEEE CEC or FUZZ-IEEE in 2021 must be submitted as soon as possible, and no later than March 15, 2018. Policies, procedures and budget worksheet for such proposals are available at http://cis.ieee.org/policies-a-procedures-for-requesting-ieee-cis-conference-support.html. More detailed guidelines can be obtained upon request to Bernadette Bouchon-Meunier (bernadette.bouchon-meunier@lip6.fr).
News and announcements from the IEEE Computational Intelligence Society
Sunday, 24 December 2017
Call for proposals for the organization of IEEE CEC or FUZZ-IEEE in 2021
Proposals for the organization of IEEE CEC or FUZZ-IEEE in 2021 must be submitted as soon as possible, and no later than March 15, 2018. Policies, procedures and budget worksheet for such proposals are available at http://cis.ieee.org/policies-a-procedures-for-requesting-ieee-cis-conference-support.html. More detailed guidelines can be obtained upon request to Bernadette Bouchon-Meunier (bernadette.bouchon-meunier@lip6.fr).
Saturday, 23 December 2017
CFP: WCCI Special Session on Brain Storm Optimization Algorithms
2018 IEEE Congress on Evolutionary Computation (IEEE CEC)
Overview
The Brain Storm Optimization (BSO)
algorithm is a new kind of swarm intelligence algorithm, which is based on the
collective behaviour 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'' optimum 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.
BSO possess two kinds of functionalities:
capability learning and capacity developing. The divergent operation
corresponds to the capability learning while the convergent operation
corresponds to capacity developing. The capacity developing focuses on moving
the algorithm's search to the area(s) where higher potential solutions may
exist while the capability learning focuses on its actual search towards new
solution(s) from the current solution for single point based optimization
algorithms and from the current population of solutions for population-based
swarm intelligence algorithms. The capability learning and capacity developing
recycle to move individuals towards better and better solutions. The BSO
algorithm, therefore, can also be called as a developmental brain storm
optimization algorithm.
The capacity developing is a top-level
learning or macro-level learning methodology. The capacity developing describes
the learning ability of an algorithm to adaptively change its parameters,
structures, and/or its learning potential according to the search states of the
problem to be solved. In other words, the capacity developing is the search
potential possessed by an algorithm. The capability learning is a bottom-level
learning or micro-level learning. The capability learning describes the ability
for an algorithm to find better solution(s) from current solution(s) with the
learning capacity it possesses.
The BSO algorithm can also 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.
Topics of Interest
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:
Ø Theoretical
aspects of BSO algorithms;
Ø Analysis
and control of BSO parameters;
Ø Parallelized
and distributed realizations of BSO algorithms;
Ø BSO for
multiple/many objective optimization;
Ø BSO for
constrained optimization;
Ø BSO for
discrete optimization;
Ø BSO for
large-scale optimization;
Ø BSO
algorithm with data mining techniques;
Ø BSO in
uncertain environments;
Ø BSO for
real-world applications.
Submission
Please follow the IEEE CEC2018 instruction for
authors and submit your paper via the IEEE CEC 2018 online submission system.
Please specify that your paper is for the Special Session on Brain Storm
Optimization Algorithms.
Organisers
CFP: WCCI Special Session on Transfer Learning in Evolutionary Computation
2018 IEEE Congress on Evolutionary Computation (WCCI2018/CEC2018)
Windsor Convention Centre, Rio de Janeiro, Brazil, 8-13 July, 2018
Data mining, machine learning, and optimisation algorithms have achieved promises in many real-world tasks, such as classification, clustering and regression. These algorithms can often generalise well on data in the same domain, i.e. drawn from the same feature space and with the same distribution. However, in many real-world applications, the available data are often from different domains. For example, we may need to perform classification in one target domain, but only have sufficient training data in another (source) domain, which may be in a different feature space or follow a different data distribution. 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 new learning framework and hot topic in data mining and machine learning.
Aim and Scope:
Evolutionary computation techniques have been successfully applied to many real-world problems, and started to be used to solve transfer learning tasks. Meanwhile, transfer learning has attracted increasing attention from many disciplines, and has been used in evolutionary computation to address complex and challenging issues. The theme of this special session is transfer learning in evolutionary computation, covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Evolutionary Multi-objective optimization (EMO) and Memetic computing (MC).
The aim is to investigate in both the new theories and methods on how transfer learning can be achieved with different evolutionary computation paradigms, and how transfer learning can be adopted in evolutionary computation, and the applications of evolutionary computation and transfer learning in real-world problems.
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 transfer learning
- Evolutionary unsupervised transfer learning
- Evolutionary semi-supervised transfer learning
- Domain adaptation and domain generalization in evolutionary computation
- Instance based transfer approaches in evolutionary computation
- Feature based transfer learning in evolutionary computation
- Parameter/model based transfer learning in evolutionary computation
- Relational based transfer learning in evolutionary computation
- Transfer learning in in evolutionary computation for classification
- Transfer learning in in evolutionary computation for regression
- Transfer learning in in evolutionary computation for clustering
- Transfer learning in in evolutionary computation for other data mining tasks, such as association rules and link analysis
- Transfer learning in in evolutionary computation for scheduling and combinatorial optimisation tasks
- Hybridisation of evolutionary computation and neural networks, and fuzzy systems for transfer learning
- Hybridisation of evolutionary computation and machine learning, information theory, statistics, etc., for transfer learning
- Transfer learning in evolutionary computation for real-world applications, e.g. text mining, image analysis, face recognition, WiFi localisation, etc.
Important dates:
- Deadline for submission of full papers: 15 January 2018
- Notification of acceptance: 15 March 2018
- Deadline for camera-ready submission: 1 May 2018
- Conference dates: 8-13 July, 2018
Paper Submission:
Please follow the IEEE WCCI/CEC2018 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to SS08 Transfer Learning in Evolutionary Computation. All papers accepted and presented at WCCI/CEC2018 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.Organisers:
Bing Xue
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Bing.Xue@ecs.vuw.ac.nz
Phone: +64-4-463 5542; Fax: +64-4-463 5045.
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Bing.Xue@ecs.vuw.ac.nz
Phone: +64-4-463 5542; Fax: +64-4-463 5045.
Liang Feng
College of Computer Science, Chongqing University, China.
liangf@cqu.edu.cn
Phone: +86-23-65102502
College of Computer Science, Chongqing University, China.
liangf@cqu.edu.cn
Phone: +86-23-65102502
Yew-Soon Ong
School of Computer Science and Engineering, Nanyang Technological University, Singapore.
asysong@ntu.edu.sg
Phone: +65-6790-5778, Fax: +65-6792-6559
School of Computer Science and Engineering, Nanyang Technological University, Singapore.
asysong@ntu.edu.sg
Phone: +65-6790-5778, Fax: +65-6792-6559
Mengjie Zhang
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Mengjie.Zhang@ecs.vuw.ac.nz
Phone: +64-4-463 5654; Fax: +64-4-463 5045
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand.
Mengjie.Zhang@ecs.vuw.ac.nz
Phone: +64-4-463 5654; Fax: +64-4-463 5045
Thursday, 21 December 2017
CFP: WCCI Special Session on Fireworks Algorithm and Its Applications
http://www.cil.pku.edu.cn/FWA_CEC/index.html
IEEE World Congress on Computational Intelligence 2018
WCCI 2018 Congress on Evolutionary Computation - CEC 2018
Fireworks Algorithm (FWA) has become one of the promising swarm intelligence algorithms in recent years, and received extensive attentions from many researchers and practitioners, because it has shown a great success in solving many complex optimization problems, especially for multi-modal optimization problems frequently happened in a lot of real-world applications. Compared with many current SI algorithms, FWA is of a new explosive search manner and probably has a fine structure of search in the solution space. As a result, it shows a strong capability of optimization computation in many optimization problems. Till to date, it has many effective variants and huge amount of successful applications. Furthermore, FWA is suitable for parallelization and works significantly better than other SI algorithms.
The aim of this special session is to bring together the experts, active researchers and newcomers from either academia or industry over the world to discuss some important issues of fireworks algorithm and its progress. All of the latest work and achievements related FWA are all welcome to this special session under the umbrella of the IEEE Congress of Evolutionary Computation at the IEEE WCCI-2018.
Topics:
Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:
Theoretical analysis of FWA
Algorithmic improvements of FWA
FWA for single-, multi-, and many-objective optimization
FWA for data mining
FWA for machine learning
FWA for data analysis
Parallelized realizations of FWA
Distributed realizations of FWA
Applications of FWA
Important Dates:
Paper submission: January 15, 2018
Paper acceptance: March 15, 2018
Final submission: May 1, 2018
Early registration: May 1, 2018
Submit a paper to the special session:
1. Click Here: http://ieee-cis.org/conferences/cec2018/upload.php
2.Follow the webpage indications and fill all the fields in the form.
3.Select Special Session on Fireworks Algorithm and Its Applications.
Organizers:
Prof. Ying Tan (ytan@pku.edu.cn)
Ying Tan is a professor at Key Laboratory of Machine Perception (MOE), Peking University, China, and a professor at Kyushu University, Japan. His primary research interests are in computational intelligence, swarm intelligence, fireworks algorithm, machine learning algorithms and their applications.
Prof. Hideyuki TAKAGI (takagi@design.kyushu-u.ac.jp)
Hideyuki TAKAGI is a professor and chair of Department of Art and Information Design at Faculty of Design of Kyushu University, Japan. His primary research interests are in Interactive Evolutionary Computation (IEC), computational intelligence, especially cooperative technology of neural networks, fuzzy systems, evolutionary computation and human.
IEEE World Congress on Computational Intelligence 2018
WCCI 2018 Congress on Evolutionary Computation - CEC 2018
Fireworks Algorithm (FWA) has become one of the promising swarm intelligence algorithms in recent years, and received extensive attentions from many researchers and practitioners, because it has shown a great success in solving many complex optimization problems, especially for multi-modal optimization problems frequently happened in a lot of real-world applications. Compared with many current SI algorithms, FWA is of a new explosive search manner and probably has a fine structure of search in the solution space. As a result, it shows a strong capability of optimization computation in many optimization problems. Till to date, it has many effective variants and huge amount of successful applications. Furthermore, FWA is suitable for parallelization and works significantly better than other SI algorithms.
The aim of this special session is to bring together the experts, active researchers and newcomers from either academia or industry over the world to discuss some important issues of fireworks algorithm and its progress. All of the latest work and achievements related FWA are all welcome to this special session under the umbrella of the IEEE Congress of Evolutionary Computation at the IEEE WCCI-2018.
Topics:
Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:
Theoretical analysis of FWA
Algorithmic improvements of FWA
FWA for single-, multi-, and many-objective optimization
FWA for data mining
FWA for machine learning
FWA for data analysis
Parallelized realizations of FWA
Distributed realizations of FWA
Applications of FWA
Important Dates:
Paper submission: January 15, 2018
Paper acceptance: March 15, 2018
Final submission: May 1, 2018
Early registration: May 1, 2018
Submit a paper to the special session:
1. Click Here: http://ieee-cis.org/conferences/cec2018/upload.php
2.Follow the webpage indications and fill all the fields in the form.
3.Select Special Session on Fireworks Algorithm and Its Applications.
Organizers:
Prof. Ying Tan (ytan@pku.edu.cn)
Ying Tan is a professor at Key Laboratory of Machine Perception (MOE), Peking University, China, and a professor at Kyushu University, Japan. His primary research interests are in computational intelligence, swarm intelligence, fireworks algorithm, machine learning algorithms and their applications.
Prof. Hideyuki TAKAGI (takagi@design.kyushu-u.ac.jp)
Hideyuki TAKAGI is a professor and chair of Department of Art and Information Design at Faculty of Design of Kyushu University, Japan. His primary research interests are in Interactive Evolutionary Computation (IEC), computational intelligence, especially cooperative technology of neural networks, fuzzy systems, evolutionary computation and human.
Wednesday, 20 December 2017
CFP: WCCI Special Session on Computational Intelligence in Data Engineering and Its Applications to Real-World Problems
IEEE World Congress on Computational Intelligence
8th -13th July 2018
Rio de Janeiro, Brazil
Computational Intelligence (CI), Artificial Intelligence (AI), Data Science and their applications are research areas jointly aligned to benefit research community and society. AI and Data Science encompass a broad field of Computational Intelligence disciplines including data mining, machine learning, ensemble learning, deep learning, fuzzy systems, and evolutionary computation, self-organizing systems and expert systems. In recognition of the escalating importance and relevance of examining the processes and results associated with obtaining and managing data, as well as scrubbing, exploring, modelling, interpreting, communicating and visualising data across all research domains, including Health, Education, Environment, Medicine, Security, Science, Technology, Business, the Humanities and the Arts, the aim of this Special Session is to allow researchers to communicate their high quality, original ideas by presenting and publishing new advances in computational intelligence to data science, engineering, internet of everything, internet of urgent things and their applications.
The world is moving through the fourth industrial revolution, which is happening all around us and affecting and changing the way we live, work and communicate with each other and the other devices around us. Widely used a new generation of artificial intelligence in intelligent medicine, smart city, robotics, intelligent manufacturing, intelligent energy, national defence and other fields will increase the core of computation intelligence and AI industry scale within the next decade. This session is dedicated to researchers and practitioners interested in strategies, theories, practices and tools, exchanging new theoretical, technical and experimental design. It focuses on CI and AI real-world applications and different use cases of solid findings and insights, best practices and applications to real-life situations, and reviewing new opportunities and frameworks for Data Sciences. This special session brings together CI, AI researchers and practitioners from different scientific disciplines with the goal of fostering collaboration between different and research groups. We aim to increase the understanding and use of AI techniques in the application to real world problems. We welcome contributions that deal with all aspects of the scientific foundations, theories, techniques and applications of computing, data and analytics, including but not limited to:
o Internet of Everything and Evolutionary computation
o AI Techniques Applied to Environmental Sciences
o Internet of Urgent things and its applications
o AI Techniques in Support of Aviation and Aerospace Op-erations
o Intelligent Approaches for Internet of Everything and its application
o Internet of Everything to support Smart Cities
o Computational Intelligence for Clinical Data Analysis
o Machine Learning and its application for Decision Making
o Machine Learning Applications in the Energy Sector
o Deep learning methods for Diagnostic Decision Support
o Novel data processing and analytics, tools and systems
o Advances in Neural networks and its applications
o Fuzzy systems and uncertainty management
o Ensemble learning for Big data mining and knowledge dis-covery
o Innovative methods for Big data complexity management
o Big Data Engineering
o Evolutionary Inspired Algorithms and Expert Systems
o Biomedical Intelligence, Health Informatics and Intelligent Driven Systems
o Linear and non-linear Learning Approaches
o Advances in Medical Image and Signal Processing
Submission of Papers:
Prospective authors are invited to submit full-length papers (not exceeding 8 pages) by 15th January 2018. Submitted papers should conform to the IEEE format and will be handled and processed electronically via the IEEE CEC 2018 online submission system. Submission implies the willingness of at least one of the authors to register and present their paper. Further details can be found at http://dese.org.uk/IEEE-wcci/
Important Dates:
Submission of Papers – 15th January 2018
Notification of Acceptance – 15th March 2018
Final Submission – 1st May 2018
Early Registration – 1st May 2018
CFP: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018)
You are invited to submit papers for the 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018) to be held in Ottawa,Ontario, Canada from June 12-14, 2018. The conference is dedicated to all aspects of computational intelligence, virtual environments and human-computer interaction technologies for measurement systems and related applications.
Papers are solicited on, but not restricted to the following topics:
Papers are solicited on, but not restricted to the following topics:
|
CFP: WCCI Special Session on Evolutionary Multi-objective Optimization based on Decomposition
ADEMO 2018: Advances in Decomposition-based Evolutionary Multi-objective Optimization
2nd Special Session on Evolutionary Multi-objective Optimization based on Decomposition @ IEEE-WCCI/CEC 2018
8-13 July 2018 – IEEE WCCI 2018, Rio de Janeiro, Brazil
*** Scope
The purpose of this special session is to promote the design, study, and validation of generic approaches for solving multi-objective optimization problems based on the concept of decomposition. Decomposition-based Evolutionary Multi-objective Optimization (DEMO) encompasses any technique, concept or framework that takes inspiration from the "divide and conquer" paradigm, by essentially breaking a multi-objective optimization problem into several subproblems for which solutions for the original global problem are computed and aggregated in a cooperative manner.
We encourage contributions reporting advances with respect to other decomposition techniques operating in the decision space or other hybrid approaches taking inspiration from operations research and mathematical programming. Many different DMOEAs variants have been proposed, studied and applied to various application domains. However, DEMOs are still in their very early infancy, since only a few basic design principles have been established compared to the huge body of literature dedicated to other well-established approaches (e.g. Pareto ranking, indicator-based techniques, etc). The main goal of the proposed session is to encourage research studies that systematically investigate the critical issues in DMOEAs at the aim of understanding their key ingredients and their main dynamics, as well a to develop solid and generic principles for designing them. The long-term goal is to contribute to the emergence of a general and unified methodology for the design, the tuning and the performance assessment of DEMOs.
*** Topics of interests
The topics of interests include (but are not limited to) the following issues:
1. Analysis of algorithmic components and performance assessment of DEMO approaches
Experimental and theoretical investigations on the accuracy of the underlying decomposition strategies, e.g. scalarizing functions techniques, multiple reference points, variable grouping, etc.
2.Adaptive, selfadaptive, and tuning aspects for the parameter setting and configuration of DEMO approaches.
3. Design and analysis of new DEMO approaches dedicated to specific combinatorial, constrained and/or continuous domains.
4. Effective hybridization of single-objective solvers with DEMO approaches, i.e., plug and play algorithms based on traditional single objective evolutionary algorithms and meta heuristics, such as: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Covariance Matrix Evolution Strategy (CMAES), Scatter Search (SS), etc.
5. Adaptation and analysis of DEMO approaches in the context of large scale and many objective problem solving
6. Application of DEMO for solving real-world problems.
7. Design and implementation of DEMO approaches in massively parallel and large scale distributed environment (e.g., GPUs, Clusters, Grids, etc).
8. Software tools for the design implementation and performance assessment of DEMO approaches
*** Deadlines and Submission
Submission Deadline: Jan 15, 2018
Notification Due: Mar 15, 2018
Final Version Due: May 1, 2018
Submission procedure, deadlines, and paper format are same than the IEEE-WCCI/CEC'18 main conference. In particular, we recall that papers must be submitted through the IEEE WCCI 2018 online submission system while selecting the ADEMO special session under the list of research topics in the submission system.
*** Organizers and Contact
--Saúl Zapotecas-Martínez (saul.zapotecas [at] gmail.com)
Universidad Autónoma Metropolitana (UAM), Cuajimalpla, México
--Bilel Derbel (bilel.derbel [at] univlille1.fr)
University Lille 1, CRIStAL CNRS UMR9189, France
DOLPHIN, Inria Lille Nord Europe, France
--Qingfu Zhang (qingfu.zhang [at] cityu.edu.hk)
City University of Hong Kong, Hong Kong
Tuesday, 19 December 2017
CFP: WCCI Special Session on Nature-Inspired Constrained Optimization
http://www.lania.mx/~emezura/sites/cec2018/index.html
2018 IEEE World Congress on Computational Intelligence
http://www.ecomp.poli.br/~wcci2018/
8-13 July 2018
Aim and scope:
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
Important Dates:
• 15th January 2018 – Paper Submission
• 15th March 2018 – Paper Acceptance
• 1st May 2018 – Final Paper Submission
Updates at: http://www.ecomp.poli.br/~wcci2018/submissions/#Importantdate
Paper submission:
Submission website: http://ieee-cis.org/conferences/cec2018/upload.php
Please choose as Main Research topic: "SS15. Nature-Inspired Constrained Optimization"
Organizers:
Efrén Mezura-Montes
Artificial Intelligence Research Center, University of Veracruz, MEXICO
Helio J.C. Barbosa
Laboratório Nacional de Computação Científica (LNCC), BRAZIL Universidade Federal de Juiz de Fora, BRAZIL
Rituparna Datta
Graduate School of Knowledge Service Engineering, Department of Industrial & Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST).
2018 IEEE World Congress on Computational Intelligence
http://www.ecomp.poli.br/~wcci2018/
8-13 July 2018
Aim and scope:
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
Important Dates:
• 15th January 2018 – Paper Submission
• 15th March 2018 – Paper Acceptance
• 1st May 2018 – Final Paper Submission
Updates at: http://www.ecomp.poli.br/~wcci2018/submissions/#Importantdate
Paper submission:
Submission website: http://ieee-cis.org/conferences/cec2018/upload.php
Please choose as Main Research topic: "SS15. Nature-Inspired Constrained Optimization"
Organizers:
Efrén Mezura-Montes
Artificial Intelligence Research Center, University of Veracruz, MEXICO
Helio J.C. Barbosa
Laboratório Nacional de Computação Científica (LNCC), BRAZIL Universidade Federal de Juiz de Fora, BRAZIL
Rituparna Datta
Graduate School of Knowledge Service Engineering, Department of Industrial & Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST).
CFP: WCCI Special Session on Differential Evolution: Past, Present and Future
2018 IEEE World Congress on Computational Intelligence (WCCI 2018)
July 8-13, 2018, Rio de Janeiro, Brazil
Aim and Scope
Differential evolution (DE) emerged as a simple and powerful stochastic real-parameter optimizer more than two decades ago and has now developed into one of the most promising research areas in the field of evolutionary computation. The success of DE has been ubiquitously evidenced in various problem domains, e. g., continuous, combinatorial, mixed continuous-discrete, single-objective, multi-objective, constrained, large-scale, multimodal, dynamic and uncertain optimization problems. Furthermore, the remarkable efficacy of DE in real-world applications significantly boosts its popularity.
Over the past decades, numerous studies on DE have been carried out to improve the performance of DE, to give a theoretical explanation of the behavior of DE, to apply DE and its derivatives to solve various scientific and engineering problems, as demonstrated by a huge number of research publications on DE in the forms of monographs, edited volumes and archival articles. Consequently, DE related algorithms have frequently demonstrated superior performance in challenging tasks. It is worth noting that DE has always been one of the top performers in previous competitions held at the IEEE Congress on Evolutionary Computation. Nonetheless, the lack of systematic benchmarking of the DE related algorithms in different problem domains, the existence of many open problems in DE, and the emergence of new application areas call for an in-depth investigation of DE.
This special session aims at bringing together researchers and practitioners to review and re-analyze past achievements, to report and discuss latest advances, and to explore and propose future directions in this rapidly emerging research area. Authors are invited to submit their original and unpublished work in the areas including, but not limited to:
· DE for continuous, discrete, mixed, single-objective, multi-objective, constrained, large-scale, multiple optima seeking (niching), dynamic and uncertain optimization
· Review, comparison and analysis of DE in different problem domains
· Experimental design and empirical analysis of DE
· DE-variants for handling mixed-integer, discrete, and binary optimization problems
· Study on initialization, reproduction and selection strategies in DE
· Study on control parameters (e.g., scale factor, crossover rate, and population size) in DE
· Self-adaptive and tuning-free DE
· Parallel and distributed DE
· Theoretical analysis and understanding of DE
· Synergy of DE with neuro-fuzzy and machine learning techniques
· DE for expensive optimization problems
· Hybridization of DE with other optimization techniques
· Interactive DE
· Application of DE to real-world problems
Important Dates
· Paper submission deadline: Janurary 15, 2018
· Paper acceptance notification date: March 15, 2018
· Final paper submission deadline: May 1, 2018
Please refer to http://www.ecomp.poli.br/~wcci2018/submissions/#Importantdate for the latest information.
Paper Submission
All papers should be submitted electronically through: http://ieee-cis.org/conferences/cec2018/upload.php
When you submit your papers to our special session, please select "SS28: Differential Evolution: Past, Present and Future" as the Main Research Topic*.
Special Session Co-Chairs
Kai Qin
Swinburne University of Technology, Australia
Swagatam Das
Electronics and Communication Sciences Unit, Indian Statistical Institute, India
Rammohan Mallipeddi
School of Electronics Engineering, Kyungpook National University, Daegu, South Korea
Efrén Mezura Montes
Artificial Intelligence Research Center, University of Veracruz, MEXICO
Email: emezura@uv.mx
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