Special Session on Real

2010 IEEE Congress on Evolutionary Computation Special Session on:

Niching Methods for Multimodal Optimization

July 18 - 23, 2010, Barcelona, Spain

Overview

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 subpopulations 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 We invite authors to submit original and unpublished work on niching methods.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.

Paper Submission:

Manuscripts should be prepared according to the standard format and page limit of regular papers specified in CEC2010 and submitted through the website http://www.wcci2010.org/. Special session papers will be treated in the same way as regular papers and included in the conference proceedings.