Investigation of three genotypes for mixed variable evolutionary optimization

Rajan Filomeno Coelho, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera, Weihong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

While the handling of optimization variables directly expressed by numbers (continuous, discrete, or integer) is abundantly investigated in the literature, the use of nominal variables is generally overlooked, despite its practical interest in plenty of scientific and industrial applications. For example, in civil engineering, the designers of a structure made out of beams might have to select the best cross-section shapes among a list of available geometries (square, circular, rectangular, etc.), which can be modeled by nominal data. Therefore, in the context of singleand multi-objective evolutionary optimization for mixed variables, this study investigates three genetic encodings (binary, real, and real-simplex) for the representation of mixed variables involving both continuous and nominal parameters. The comparison of the genotypes combined with the instances of crossover is performed on six analytical benchmark test functions, as well as on the multi-objective design optimization of a six-storey rigid frame, showing that for mixed variables, real (and to a lesser extent: real-simplex) coding provides the best results, especially when combined with a uniform crossover.

Original languageEnglish
Title of host publicationAdvances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences
EditorsDavid Greiner, Blas Galván, Gabriel Winter, Jacques Périaux, Jacques Périaux, Nicolas Gauger, Kyriakos Giannakoglou
PublisherSpringer Science and Business Media B.V.
Pages309-319
Number of pages11
ISBN (Print)9783319115405
DOIs
StatePublished - 2015
Event10th International Conference on Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, 2013 - Las Palmas, Spain
Duration: 7 Oct 20139 Oct 2013

Publication series

NameComputational Methods in Applied Sciences
Volume36
ISSN (Print)1871-3033

Conference

Conference10th International Conference on Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, 2013
Country/TerritorySpain
City Las Palmas
Period7/10/139/10/13

Keywords

  • Categorical variables
  • Evolutionary algorithms
  • Genotype
  • Mixed variables

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