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GECCO
2007
Springer

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

9 years 5 months ago
Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover
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. Categories and Subject Descriptors I.2.8 [Artificial intelligence]: Problem Solving, Control Methods, and Search
Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sen
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff, Edward P. K. Tsang
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