GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

Authors:
J. M. Peña;V. Robles;P. Larrañaga;V. Herves;F. Rosales;M. S. Pérez
Affiliations:
Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain;Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain;Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain;Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain;Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain;Universidad Politécnica de Madrid, Madrid, Spain and Universidad del País Vasco, San Sebastián, Spain
Venue:
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Year:
2004

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Abstract

Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GA-EDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. In order to perform an evaluation of this new approach a selection of synthetic optimizations problems have been proposed together with two real-world cases. Experimental results show the correctness of our new approach.