Improved topological niching for real-valued global optimization

Preuss M.


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. © 2012 Springer-Verlag.



Publication type
Conference Paper

Peer reviewed
Yes

Publication status
Published

Year
2012

Conference
EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, EvoApplications 2012

Venue
Malaga, esp

Start page
386

End page
395

Pages range
386-395

Volume
null

Title of series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Language
English

ISSN
1611-3349

ISBN
9783642291777

DOI

Full text

Affiliation
Universitat Dortmund