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Bioprocess Biosyst Eng. 2006 Dec;29(5-6):385-90. Epub 2006 Oct 18.

Genetic algorithm for multi-objective experimental optimization.

Author information

1
Lehrstuhl für Bioverfahrenstechnik, Technische Universität München, Boltzmannstr. 15, Garching 85748, Germany.

Abstract

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).

PMID:
17048033
PMCID:
PMC1705497
DOI:
10.1007/s00449-006-0087-7
[Indexed for MEDLINE]
Free PMC Article
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