Genetic algorithm for multi-objective experimental optimization

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 eva...

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Detalles Bibliográficos
Autores principales: Link, Hannes, Weuster-Botz, Dirk
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2006
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1705497/
https://www.ncbi.nlm.nih.gov/pubmed/17048033
http://dx.doi.org/10.1007/s00449-006-0087-7
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author Link, Hannes
Weuster-Botz, Dirk
author_facet Link, Hannes
Weuster-Botz, Dirk
author_sort Link, Hannes
collection PubMed
description 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).
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spelling pubmed-17054972006-12-18 Genetic algorithm for multi-objective experimental optimization Link, Hannes Weuster-Botz, Dirk Bioprocess Biosyst Eng Original Paper 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). Springer-Verlag 2006-10-18 2006-12 /pmc/articles/PMC1705497/ /pubmed/17048033 http://dx.doi.org/10.1007/s00449-006-0087-7 Text en © Springer-Verlag 2006
spellingShingle Original Paper
Link, Hannes
Weuster-Botz, Dirk
Genetic algorithm for multi-objective experimental optimization
title Genetic algorithm for multi-objective experimental optimization
title_full Genetic algorithm for multi-objective experimental optimization
title_fullStr Genetic algorithm for multi-objective experimental optimization
title_full_unstemmed Genetic algorithm for multi-objective experimental optimization
title_short Genetic algorithm for multi-objective experimental optimization
title_sort genetic algorithm for multi-objective experimental optimization
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1705497/
https://www.ncbi.nlm.nih.gov/pubmed/17048033
http://dx.doi.org/10.1007/s00449-006-0087-7
work_keys_str_mv AT linkhannes geneticalgorithmformultiobjectiveexperimentaloptimization
AT weusterbotzdirk geneticalgorithmformultiobjectiveexperimentaloptimization

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