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Trimming, ordering, and similarity check for DSMGA-II: DSMGA-II-TOS

Published: 19 July 2022 Publication History

Abstract

The dependency structure matrix genetic algorithm II (DSMGA-II) is one of the state-of-the-art model-building genetic algorithms capable of solving combinatorial optimization problems by exploiting the underlying structures of the problems. The linkage model generates a series of masks for trial to recombine genes among chromosomes via optimal mixing. This paper proposes three improvements that adaptively adjust the scope, the order, and the receivers of trials. Specifically, the mean of the mask sizes from previous successful recombinations is used to limit the maximum sizes of later trials. Also, successful recombinations prioritize the corresponding mask sizes of trials. Finally, recombinations are confined between chromosomes that pass the proposed similarity check. The ablation study indicates that each proposed technique is indispensable. Combined with these three improvements, DSMGA-II-2E empirically consumes fewer function evaluations on most of the test problems.

References

[1]
Peter AN Bosman and Dirk Thierens. 2012. Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. In Proceedings of the 14th annual conference on Genetic and evolutionary computation. 585--592.
[2]
Ping-Lin Chen, Chun-Jen Peng, Chang-Yi Lu, and Tian-Li Yu. 2017. Two-edge graphical linkage model for DSMGA-II. In Proceedings of the Genetic and Evolutionary Computation Conference. 745--752.
[3]
David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York.
[4]
Shih-Huan Hsu and Tian-Li Yu. 2015. Optimization by pairwise linkage detection, incremental linkage set, and restricted/back mixing: DSMGA-II. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 519--526.
[5]
Dirk Thierens. 2010. The linkage tree genetic algorithm. In International Conference on Parallel Problem Solving from Nature. Springer, 264--273.
[6]
Dirk Thierens and Peter AN Bosman. 2011. Optimal mixing evolutionary algorithms. In Proceedings of the 13th annual conference on Genetic and evolutionary computation. 617--624.

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cover image ACM Conferences
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2022
2395 pages
ISBN:9781450392686
DOI:10.1145/3520304
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2022

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Author Tags

  1. estimation of distribution algorithms
  2. genetic algorithms
  3. model building
  4. optimal mixing

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  • ministry of science and technology in Taiwan

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GECCO '22
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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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