人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
論文
多目的関数最適化におけるGAと局所探索の組み合わせ: GA then LSの推奨
原田 健池田 心佐久間 淳小野 功小林 重信
著者情報
ジャーナル フリー

2006 年 21 巻 6 号 p. 482-492

詳細
抄録

It is well known that local search (LS) improves the performance of genetic algorithms (GA) in single objective optimization, and it has recently been reported that the hybridization of GA with LS is effective in multiobjective combinatorial optimization as well. In most studies of this kind, LS is applied to the solutions of each generation of GA, which is the scheme called ``GA with LS'' herein. Another scheme, in which LS is applied to the solutions obtained with GA, has also been studied, which is called ``GA then LS'' herein. It seems there is no consensus in the literature as to which scheme is better. The situation in the multibojective function optimization literature is even more unclear since the number of such studies in the field has been small. However, some argue that LS contributes marginally to improving the performance of GA in multiobjective function optimization.
This paper, assuming that objective functions are differentiable, reveals the reasons why GA is not necessarily effective in finding solutions of high precision, and hence hybridizing it with LS is indeed effective in multiobjective function optimization. It also suggests that the hybridization scheme which maximally exploits both GA and LS is GA then LS. Experiments confirmed that GA is not suitable for obtaining solutions of high precision, and GA then LS performs better than GA and GA with LS on many benchmark problems.

著者関連情報
© 2006 JSAI (The Japanese Society for Artificial Intelligence)
前の記事 次の記事
feedback
Top