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[CFP] 2014 IEEE CEC Special Session on: Niching Methods for Multimodal Optimization

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  • Michael Epitropakis
    *** Merry Christmas and a Peaceful New Year *** ** Apologies for multiple postings ** ** Please kindly forward to those who may be interested ** *** SUBMISSION
    Message 1 of 2 , Dec 23, 2013
    • 0 Attachment
      *** Merry Christmas and a Peaceful New Year ***

      ** Apologies for multiple postings **
      ** Please kindly forward to those who may be interested **

      *** SUBMISSION DEADLINE EXTENDED: January 20, 2014 (final deadline)***

      ################
      Call for Papers
      ################

      2014 IEEE Congress on Evolutionary Computation Special Session on: Niching Methods for Multimodal Optimization

      July 6 - 11, 2014, Beijing, China
      URL: http://goanna.cs.rmit.edu.au/~xiaodong/cec14-niching/index.html

      ===========
      Objectives
      ===========

      Population-based meta-heuristic algorithms such as Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are "multimodal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions so that a decision maker can choose one that is most proper in his/her problem domain. Numerous techniques have been developed in the past for locating multiple optima (global or local). These techniques are commonly referred to as "niching" methods. A niching method can be incorporated into a standard EA to promote and maintain formation of multiple stable sub-populations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions. Many niching methods have been developed in the past, including crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc.

      Most of existing niching methods, however, have difficulties which need to be overcome before they can be applied successfully to real-world multimodal problems. Some identified issues include: difficulties to pre-specify some niching parameters; difficulties in maintaining found solutions in a run; extra computational overhead; poor scalability when dimensionality is high. This special session aims to highlight the latest developments in niching methods, bring together researchers from academia and industries, and explore future research directions on this topic. We invite authors to submit original and unpublished work on niching methods. Topics of interest include but are not limited to:

      - Theoretical developments in multimodal optimization
      - Niching methods that incurs lower computational costs
      - Handling the issue of niching parameters in niching methods
      - Handling the scalability issue in niching methods
      - Handling problems characterized by massive multi-modality
      - Adaptive or parameter-less niching methods
      - Multiobjective approaches to niching
      - Multimodal optimization in dynamic environments
      - Niching methods applied to discrete multimodal optimization problems
      - Niching methods applied to constrained multimodal optimization problems
      - Niching methods using parallel or distributed computing techniques
      - Benchmarking niching methods, including test problem design and performance metrics
      - Comparative studies of various niching methods
      - Niching methods applied to engineering and other real-world multimodal optimization problems

      Please note that we are NOT interested if the adopted task is to find a single solution of a multimodal problem.


      Furthermore, a previously proposed benchmark suite may help the interested researchers to evaluate and compare their niching algorithms with the state-of-the-art methodologies in the field. Further information can be found in the IEEE CEC' 2013 "Competition on Niching Methods for Multimodal Optimization" web site: http://goanna.cs.rmit.edu.au/~xiaodong/cec13-niching/competition/ . Briefly, the aim of this benchmark suite is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The benchmark suite allows researchers to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty.


      ================
      Important Dates
      ================

      - Paper Submission: 20 January 2014
      - Decision Notification: 15 March 2014
      - Final Paper Submission: 15 April 2014

      ================
      Paper Submission
      ================

      Manuscripts should be prepared according to the standard format and page limit of regular papers specified in WCCI'2014 and submitted through the WCCI'2014 website: http://www.ieee-wcci2014.org/. Special session papers will be treated in the same way as regular papers and included in the conference proceedings.

      ===================
      Technical Committee
      ===================

      PatrickĀ  Siarry, Universite Paris-Est Creteil Val-de-Marne, France
      Jing Liang, Zhengzhou University, China
      Matthys du Plessis, Nelson Mandela Metropolitan University, South Africa
      Konstantinos E. Parsopoulos, University of Ioannina, Greece
      Jonathan Mwaura, University of Pretoria, South Africa
      Mike Preuss, Dortmund University, Germany
      Nicos Pavlidis, Lancaster University, UK
      Vassilis P. Plagianakos, University of Central Greece, Greece
      Jian-Ping Li, Bradford University, UK
      Bruno Sareni, Universite de Toulouse, France
      Ofer Shir, Haifa Research Lab, Israel
      Grant Dick, University of Otago, NZ
      Michael N. Vrahatis, University of Patras, Greece
      More to be confirmed...

      ==========================
      Special Session Organizers
      ==========================

      Xiaodong Li, RMIT University, Australia
      Andries Engelbrecht, University of Pretoria, South Africa
      Michael G. Epitropakis, University of Stirling, Scotland, UK.

    • Michael Epitropakis
      ** Apologies for multiple postings ** ** Please kindly forward to those who may be interested ** *** SUBMISSION DEADLINE EXTENDED: January 20, 2014 (final
      Message 2 of 2 , Jan 4, 2014
      • 0 Attachment
        ** Apologies for multiple postings **
        ** Please kindly forward to those who may be interested **

        *** SUBMISSION DEADLINE EXTENDED: January 20, 2014 (final deadline)***

        ################
        Call for Papers
        ################

        2014 IEEE Congress on Evolutionary Computation Special Session on: Niching Methods for Multimodal Optimization

        July 6 - 11, 2014, Beijing, China
        URL: http://goanna.cs.rmit.edu.au/~xiaodong/cec14-niching/index.html

        ===========
        Objectives
        ===========

        Population-based meta-heuristic algorithms such as Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These algorithms typically converge to a single solution because of the global selection scheme used. Nevertheless, many real-world problems are "multimodal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions so that a decision maker can choose one that is most proper in his/her problem domain. Numerous techniques have been developed in the past for locating multiple optima (global or local). These techniques are commonly referred to as "niching" methods. A niching method can be incorporated into a standard EA to promote and maintain formation of multiple stable sub-populations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions. Many niching methods have been developed in the past, including crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc.

        Most of existing niching methods, however, have difficulties which need to be overcome before they can be applied successfully to real-world multimodal problems. Some identified issues include: difficulties to pre-specify some niching parameters; difficulties in maintaining found solutions in a run; extra computational overhead; poor scalability when dimensionality is high. This special session aims to highlight the latest developments in niching methods, bring together researchers from academia and industries, and explore future research directions on this topic. We invite authors to submit original and unpublished work on niching methods. Topics of interest include but are not limited to:

        - Theoretical developments in multimodal optimization
        - Niching methods that incurs lower computational costs
        - Handling the issue of niching parameters in niching methods
        - Handling the scalability issue in niching methods
        - Handling problems characterized by massive multi-modality
        - Adaptive or parameter-less niching methods
        - Multiobjective approaches to niching
        - Multimodal optimization in dynamic environments
        - Niching methods applied to discrete multimodal optimization problems
        - Niching methods applied to constrained multimodal optimization problems
        - Niching methods using parallel or distributed computing techniques
        - Benchmarking niching methods, including test problem design and performance metrics
        - Comparative studies of various niching methods
        - Niching methods applied to engineering and other real-world multimodal optimization problems

        Please note that we are NOT interested if the adopted task is to find a single solution of a multimodal problem.


        Furthermore, a previously proposed benchmark suite may help the interested researchers to evaluate and compare their niching algorithms with the state-of-the-art methodologies in the field. Further information can be found in the IEEE CEC' 2013 "Competition on Niching Methods for Multimodal Optimization" web site: http://goanna.cs.rmit.edu.au/~xiaodong/cec13-niching/competition/ . Briefly, the aim of this benchmark suite is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The benchmark suite allows researchers to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty.


        ================
        Important Dates
        ================

        - Paper Submission: 20 January 2014
        - Decision Notification: 15 March 2014
        - Final Paper Submission: 15 April 2014

        ================
        Paper Submission
        ================

        Manuscripts should be prepared according to the standard format and page limit of regular papers specified in WCCI'2014 and submitted through the WCCI'2014 website: http://www.ieee-wcci2014.org/. Special session papers will be treated in the same way as regular papers and included in the conference proceedings. Please select "EC11: Niching Methods for Multimodal Optimization" as the main research topic when submitting your manuscripts.

        ===================
        Technical Committee
        ===================

        PatrickĀ  Siarry, Universite Paris-Est Creteil Val-de-Marne, France
        Jing Liang, Zhengzhou University, China
        Matthys du Plessis, Nelson Mandela Metropolitan University, South Africa
        Konstantinos E. Parsopoulos, University of Ioannina, Greece
        Jonathan Mwaura, University of Pretoria, South Africa
        Mike Preuss, Dortmund University, Germany
        Nicos Pavlidis, Lancaster University, UK
        Vassilis P. Plagianakos, University of Central Greece, Greece
        Jian-Ping Li, Bradford University, UK
        Bruno Sareni, Universite de Toulouse, France
        Ofer Shir, Haifa Research Lab, Israel
        Grant Dick, University of Otago, NZ
        Michael N. Vrahatis, University of Patras, Greece
        More to be confirmed...

        ==========================
        Special Session Organizers
        ==========================

        Xiaodong Li, RMIT University, Australia
        Andries Engelbrecht, University of Pretoria, South Africa
        Michael G. Epitropakis, University of Stirling, Scotland, UK.
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