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Adapting mutation and recombination operators to range-aware relations in real-world application data

Authors:

Christina Plump,

Bernhard J. Berger,

Rolf DrechslerAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 755 - 758

https://doi.org/10.1145/3520304.3529066

Published: 19 July 2022 Publication History

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Abstract

Optimisation problems with higher-dimensional search spaces do usually not only come with equality or inequality constraints, but also with dependencies between the different variables. In real-world applications, especially in experimental data from material sciences, these relations as well as the constraints may not be true for the entire search space, but only for certain areas. Other constraints or relations may hold then for different areas of the search space. We build on correlation-aware mutation and recombination operators that are used in genetic algorithms and adjust them to be able to deal with area-specific constraints and relations. This can be configured by a domain expert using a domain-specific language. Our approach is evaluated with well-known benchmark functions, carefully designed distributions, and data description files and shows a better capability of generating feasible solutions in the population.

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Berger BPlump CPaul LDrechsler RLi XHandl J(2024)EvoAl - Codeless Domain-OptimisationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664154(1640-1648)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664154
Berger BPlump CDrechsler R(2023)EVOAL: A Domain-Specific Language-Based Approach to Optimisation2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10253985(1-10)Online publication date: 1-Jul-2023
https://doi.org/10.1109/CEC53210.2023.10253985

Index Terms

Adapting mutation and recombination operators to range-aware relations in real-world application data
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

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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Published: 19 July 2022

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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

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Berger BPlump CPaul LDrechsler RLi XHandl J(2024)EvoAl - Codeless Domain-OptimisationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664154(1640-1648)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664154
Berger BPlump CDrechsler R(2023)EVOAL: A Domain-Specific Language-Based Approach to Optimisation2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10253985(1-10)Online publication date: 1-Jul-2023
https://doi.org/10.1109/CEC53210.2023.10253985

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Differential evolution mutation operators for constrained multi-objective optimization

Constrained differential evolution with multiobjective sorting mutation operators for constrained optimization

Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms

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