A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems

Authors:
Anthony J. da C.C. Lins;Fernando B. Lima-Neto;François Fages;Carmelo J. A. Bastos-Filho
Affiliations:
Polytechnic School of Engineering of University of Pernambuco, Brazil;Polytechnic School of Engineering of University of Pernambuco, Brazil;Contraintes, INRIA Rocquencourt, France;Polytechnic School of Engineering of University of Pernambuco, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 2
Cited 0

Completely Derandomized Self-Adaptation in Evolution Strategies

Evolutionary Computation
Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems

Applied Soft Computing

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Abstract

This paper presents a comparative analyzes between three search algorithms, named Fish School Search, Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategy applied to ill-conditioned problems. We aim to demonstrate the effectiveness of the Fish School Search in the optimization processes when the objective function has ill-conditioned properties. We achieved good results for the Fish School Search and in some cases we obtained superior results when compared to the other algorithms.