Improved Topological Niching for Real-Valued Global Optimization

  • Mike Preuss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

We show how nearest-better clustering, the core component of the NBC-CMA niching evolutionary algorithm, is improved by appyling a second heuristic rule. This leads to enhanced basin identification for higher dimensional (5D to 20D) optimization problems, where the NBC-CMA has previously shown only mediocre performance compared to other niching and global optimization algorithms. The new method is integrated into a niching algorithm (NEA2) and compared to NBC-CMA and BIPOP-CMA-ES via the BBOB benchmarking suite. It performs very well on problems that enable recognizing basins at all with reasonable effort (number of basins not too high, e.g. the Gallagher problems), as expected. Beyond that point, niching is obviously not applicable any more and random restarts as done by the CMA-ES are the method of choice.

Keywords

Latin Hypercube Sampling Heuristic Rule Incoming Connection Multimodal Problem Multimodal Optimization 
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 2012

Authors and Affiliations

  • Mike Preuss
    • 1
  1. 1.Chair of Algorithm Engineering, Computational Intelligence Group, Dept. of Computer ScienceTechnische Universität DortmundDortmundGermany

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