NDLI: Genetic algorithm-based topology optimization: Performance improvement through dynamic evolution of the population size

Content Provider	IEEE Xplore Digital Library
Author	Denies, J. Dehez, B. Glineur, F. Ben Ahmed, H.
Copyright Year	2012
Description	Author affiliation: SATIE laboratory - Ecole Normale Supérieure de Cachan Antenne de Bretagne, Campus de Ker Lann, 35170, Bruz, France (Ben Ahmed, H.) \|\| CORE - Universitée catholique de Louvain, Voie du Roman Pays, 34, Louvain-la-Neuve, 1348, Belgium (Glineur, F.) \|\| CEREM - Universitée catholique de Louvain, place du levant, 3, Louvain-la-Neuve, 1348, Belgium (Denies, J.; Dehez, B.)
Abstract	Topological optimization tool using genetic algorithm as optimization algorithm are known as very expensive in computation time. In this paper, we study an approach to improve performance of topological optimization tool by introducing a dynamic variation of the population size of children during the process of optimization. This method allows to improve performance of each generation by adapting the number of children created and by introducing a coefficient of reproduction for each individual inside the population of parents. Through this coefficient of reproduction, the number of children assigns to each parent is calculated. The number of evaluations at each generation changes and the tool can saves evaluations in order to increase the number of iterations.
Starting Page	1033
Ending Page	1038
File Size	254689
Page Count	6
File Format	PDF
ISBN	9781467312998
e-ISBN	9781467313018
DOI	10.1109/SPEEDAM.2012.6264469
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis genetic algorithm inverse problem Voronoï diagram topologyoptimization Heuristic algorithms Materials design Genetics Topology Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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