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

被引：0
|
作者：
Peña, JM
Robles, V
Larrañaga, P
Herves, V
Rosales, F
Pérez, MS
机构：
[1] Univ Politecn Madrid, Madrid, Spain
[2] Univ Basque Country, E-20080 San Sebastian, Spain
来源：
INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE | 2004年 / 3029卷
关键词：
genetic algorithms and heuristic search;
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：
Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GAEDA, 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.
引用
收藏
页码：361 / 371
页数：11
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