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Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization

Published: 07 July 2007 Publication History

Abstract

In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point and uniform) tend to decrease the diversity of solutions in EMO algorithms while they improve the convergence to the Pareto front. This is because such a crossover operator, which is called geometric crossover, always generates an offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the parents. In this paper, first we propose a non-geometric binary crossover operator to generate an offspring outside the segment between its parents. Next we examine the effect of the use of non-geometric binary crossover on single-objective genetic algorithms. Experimental results show that non-geometric binary crossover improves their search ability. Then we examine its effect on EMO algorithms. Experimental results show that non-geometric binary crossover drastically increases the diversity of solutions while it slightly degrades their convergence to the Pareto front. As a result, some performance measures such as hypervolume are clearly improved.

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Cited By

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  • (2011)Effectiveness of genetic multistep searches in interpolation and extrapolation domains on multiobjective optimization2011 IEEE International Conference on Systems, Man, and Cybernetics10.1109/ICSMC.2011.6083716(669-674)Online publication date: Oct-2011
  • (2009)Effects of nongeometric binary crossover on multiobjective 0/1 knapsack problemsArtificial Life and Robotics10.1007/s10015-008-0593-613:2(434-437)Online publication date: 8-Mar-2009
  • (2008)Maintaining the diversity of solutions by non-geometric binary crossoverProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389303(1111-1112)Online publication date: 13-Jul-2008

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  1. Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2007

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    Author Tags

    1. balance between diversity and convergence
    2. diversity preserving
    3. evolutionary multiobjective optimization (EMO) algorithms
    4. multiobjective combinatorial optimization
    5. non-geometic crossover operators

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    • (2011)Effectiveness of genetic multistep searches in interpolation and extrapolation domains on multiobjective optimization2011 IEEE International Conference on Systems, Man, and Cybernetics10.1109/ICSMC.2011.6083716(669-674)Online publication date: Oct-2011
    • (2009)Effects of nongeometric binary crossover on multiobjective 0/1 knapsack problemsArtificial Life and Robotics10.1007/s10015-008-0593-613:2(434-437)Online publication date: 8-Mar-2009
    • (2008)Maintaining the diversity of solutions by non-geometric binary crossoverProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389303(1111-1112)Online publication date: 13-Jul-2008

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