Genetic algorithm for multi-objective experimental optimization

被引：21

作者：

Link, Hannes ^{[1
]}

Weuster-Botz, Dirk ^{[1
]}

机构：

[1] Tech Univ Munich, Lehrstuhl Bioverfahrenstech, D-85748 Garching, Germany

来源：

BIOPROCESS AND BIOSYSTEMS ENGINEERING | 2006年 / 29卷 / 5-6期

关键词：

experimental design; genetic algorithm; multi-objective optimization; software tool;

D O I：

10.1007/s00449-006-0087-7

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).

引用

页码：385 / 390

页数：6

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BIOPROCESS AND BIOSYSTEMS ENGINEERING, 2005, 28 (02) :109-119

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