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When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization

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GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1421 - 1422

https://doi.org/10.1145/2739482.2764666

Published: 11 July 2015 Publication History

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Abstract

We show that multistart next ascent hillclimbing compares favourably to crowding-based genetic algorithms when solving instances of the multimodal problem generator. We conjecture that it is unlikely that any practical evolutionary algorithm is capable of solving this type of problem instances faster than the multistart hillclimbing strategy.

References

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K. A. De Jong, M. A. Potter, and W. M. Spears. Using problem generators to explore the effects of epistasis. In T. Bäck, editor, Proceedings of the International Conference on Genetic Algorithms (ICGA 1997), pages 338--345, San Francisco, 1997. Morgan Kaufmann.

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G. R. Harik. Finding multimodal solutions using restricted tournament selection. Proceedings of the Sixth International Conference on Genetic Algorithms, pages 24--31, 1995.

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F. G. Lobo and M. Bazargani. When hillclimbers beat genetic algorithms in multimodal optimization. arXiv Report No. 1504.06859, 2015.

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F. G. Lobo and C. F. Lima. On the utility of the multimodal problem generator for assessing the performance of evolutionary algorithms. In M. Keijzer et al., editors, Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO-2006), pages 1233--1240. ACM Press, 2006.

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K. Sastry. Evaluation-relaxation schemes for genetic and evolutionary algorithms. Master's thesis, University of Illinois at Urbana-Champaign, Urbana, IL, 2001.

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Cited By

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Gençal MOral M(2021)Bipolar Mating Tendency: Harmony Between the Best and the Worst IndividualsArabian Journal for Science and Engineering10.1007/s13369-021-06105-5Online publication date: 1-Sep-2021
https://doi.org/10.1007/s13369-021-06105-5
Cheng SLu HGuo YLei XLiang JChen JShi Y(2019)Dynamic Multimodal Optimization: A Preliminary Study2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790078(279-285)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790078
Farias RDe Magalhaes C(2018)Parent Selection Strategies in Niching Genetic Algorithms2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477847(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/CEC.2018.8477847
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Index Terms

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Information

Published In

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

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: 11 July 2015

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Fundação para a Ciência e a Tecnologia

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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

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Cited By

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Gençal MOral M(2021)Bipolar Mating Tendency: Harmony Between the Best and the Worst IndividualsArabian Journal for Science and Engineering10.1007/s13369-021-06105-5Online publication date: 1-Sep-2021
https://doi.org/10.1007/s13369-021-06105-5
Cheng SLu HGuo YLei XLiang JChen JShi Y(2019)Dynamic Multimodal Optimization: A Preliminary Study2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790078(279-285)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790078
Farias RDe Magalhaes C(2018)Parent Selection Strategies in Niching Genetic Algorithms2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477847(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/CEC.2018.8477847
Cheng SChen JLei XShi Y(2018)Locating Multiple Optima via Brain Storm Optimization AlgorithmsIEEE Access10.1109/ACCESS.2018.28115426(17039-17049)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2811542

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