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Niching Methods: Speciation Theory Applied for Multi-modal Function Optimization

  • Ofer M. ShirEmail author
  • Thomas Bäck
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

While contemporary Evolutionary Algorithms (EAs) excel in various types of optimizations, their generalization to speciational subpopulations is much needed upon their deployment to multi-modal landscapes, mainly due to the typical loss of population diversity. The resulting techniques, known as niching methods, are the main focus of this chapter, which will provide the motivation, pose the problem both from the biological as well as computational perspectives, and describe algorithmic solutions. Biologically inspired by organic speciation processes, and armed with real-world incentive to obtain multiple solutions for better decision making, we shall present here the application of certain bioprocesses to multi-modal function optimization, by means of a broad overview of the existing work in the field, as well as a detailed description of specific test cases.

Keywords

Evolution Strategy Mutation Operator Strategy Parameter Selection Operator Search Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Natural Computing GroupLeiden UniversityLeidenThe Netherlands
  2. 2.NuTech SolutionsDortmundGermany

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