Fitness landscape approximation by adaptive support vector regression with opposition-based learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Pages1329-1334
Number of pages6
DOIs
Publication statusPublished - Dec 1 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: Oct 13 2013Oct 16 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Other

Other2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
CountryUnited Kingdom
CityManchester
Period10/13/1310/16/13

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All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction

Cite this

Pei, Y., & Takagi, H. (2013). Fitness landscape approximation by adaptive support vector regression with opposition-based learning. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 (pp. 1329-1334). [6721983] (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013). https://doi.org/10.1109/SMC.2013.230