Learning probability distributions in continuous evolutionary algorithms– a comparative review

Authors:
Stefan Kern;Sibylle D. Mü/ller;Nikolaus Hansen;Dirk Bü/che;Jiri Ocenasek;Petros Koumoutsakos
Affiliations:
Institute of Computational Science –/ ICoS;Institute of Computational Science –/ ICoS;Institute of Computational Science –/ ICoS;Institute of Computational Science –/ ICoS;Computational Laboratory –/ CoLab, Swiss Federal Institute of Technology (ETH), CH-8092 Zü/rich, Switzerland;Institute of Computational Science –/ ICoS&semi/ Computational Laboratory –/ CoLab, Swiss Federal Institute of Technology (ETH), CH-8092 Zü/rich, Switzerland (Author for corresponden ...
Venue:
Natural Computing: an international journal
Year:
2004

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Abstract

We present a comparative review of Evolutionary Algorithms that generate new population members by sampling a probability distributionconstructed during the optimization process. We present a unifying formulation for five such algorithms that enables us to characterize them based on the parametrization of the probability distribution, the learning methodology, and the use of historical information.The algorithms are evaluated on a number of test functions in order to assess their relative strengths and weaknesses. This comparative reviewhelps to identify areas of applicability for the algorithms and to guidefuture algorithmic developments.