Continuous optimization by evolving probability density functions with a two-island model

Authors:
Alicia D. Benítez;Jorge Casillas
Affiliations:
Dept. Computer Science and Artificial Intelligence, University of Granada, Spain;Dept. Computer Science and Artificial Intelligence, University of Granada, Spain
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

The work presents a new evolutionary algorithm designed for continuous optimization. The algorithm is based on evolution of probability density functions, which focus on the most promising zones of the domain of each variable. Several mechanisms are included to self-adapt the algorithm to the feature of the problem. By means of an experimental study, we have observed that our algorithm obtains good results of precision, mainly in multimodal problems, in comparison with some state-of-the-art evolutionary methods.