Global Multiobjective Optimization via Estimation of Distribution Algorithm with Biased Initialization and Crossover

被引：0
|
作者：
Zhou, Aiming [1 ]
Zhang, Qingfu [1 ]
Jin, Yaochu [2 ]
Sendhoff, Bernhard [2 ]
Tsang, Edward [1 ]
机构：
[1] Univ Essex, Dept Comp Sci, Colchester CO4 3SQ, Essex, England
[2] Honda Res Inst Europe, D-63073 Offenbach, Germany
来源：
GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 | 2007年
关键词：
global optimization; multiobjective optimization; estimation of distribution algorithm; biased initialization; biased crossover;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary agorithms. In this paper, two operators, biased initialization and biased crossover, axe 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.
引用
收藏
页码：617 / +
页数：2
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