Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover

Authors:
Aimin Zhou;Qingfu Zhang;Yaochu Jin;Bernhard Sendhoff;Edward Tsang
Affiliations:
University of Essex, Colchester, United Kingdom;University of Essex, Colchester, United Kingdom;Honda Research Institute Europe, Offenbach, Germany;Honda Research Institute Europe, Offenbach, Germany;University of Essex, Colchester, United Kingdom
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.