Local Meta-models for ASM-MOMA

被引：0

作者：

Pilat, Martin ^{[1
,2
]}

Neruda, Roman ^{[2
]}

机构：

[1] Charles Univ Prague, Fac Math & Phys, Dept Theoret Comp Sci & Math Log, Prague, Czech Republic

[2] Acad Sci Czech Republ, Inst Comp Sci, Prague 8, Czech Republic

来源：

BIO-INSPIRED COMPUTING AND APPLICATIONS | 2012年 / 6840卷

关键词：

Multiobjective optimization; meta-model; evolutionary algorithm;

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 algorithms generally require a large number of objective function evaluations which can be costly in practice. These evaluations can be replaced by evaluations of a cheaper meta-model of the objective functions. In this paper we describe a multiobjective memetic algorithm utilizing local distance based meta-models. This algorithm is evaluated and compared to standard multiobjective evolutionary algorithms as well as a similar algorithm with a global meta-model. The number of objective function evaluations is considered, and also the conditions under which the algorithm actually helps to reduce the time needed to find a solution are analyzed.

引用

页码：79 / 84

页数：6

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