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Adapting mutation and recombination operators to range-aware relations in real-world application data
Publikationstyp
Conference Paper
Publikationsdatum
2022-07
Sprache
English
Institut
Start Page
755
End Page
758
Citation
Genetic and Evolutionary Computation Conference (GECCO 2022)
Contribution to Conference
Publisher DOI
Scopus ID
2-s2.0-85136335473
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.
Schlagworte
dependencies
domain knowledge
evolutionary algorithm