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Experimental Genetic Operators Analysis for the Multi-objective Permutation Flowshop

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

The aim of this paper is to show the influence of genetic operators such as crossover and mutation on the performance of a genetic algorithm (GA). The GA is applied to the multi-objective permutation flowshop problem. To achieve our goal an experimental study of a set of crossover and mutation operators is presented. A measure related to the dominance relations of different non-dominated sets, generated by different algorithms, is proposed so as to decide which algorithm is the best. The main conclusion is that there is a crossover operator having the best average performance on a very specific set of instances, and under a very specific criterion. Explaining the reason why a given operator is better than others remains an open problem.

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Author information

Authors and Affiliations

  1. Computer Science Department, CICESE Research Center, Km 107 Carr. Tijuana-Ensenada, Ensenada, B.C., México

    Carlos A. Brizuela & Rodrigo Aceves

Authors
  1. Carlos A. Brizuela
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  2. Rodrigo Aceves
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Brizuela, C.A., Aceves, R. (2003). Experimental Genetic Operators Analysis for the Multi-objective Permutation Flowshop. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_41

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  • DOI: https://doi.org/10.1007/3-540-36970-8_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

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