A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables
Abstract This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01721-8 |
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| _version_ | 1823861480843378688 |
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| author | Jaume Jordan Javier Palanca Victor Sanchez-Anguix Vicente Julian |
| author_facet | Jaume Jordan Javier Palanca Victor Sanchez-Anguix Vicente Julian |
| author_sort | Jaume Jordan |
| collection | DOAJ |
| description | Abstract This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions, one for each neighborhood. The main goal of this work is to propose a crossover operator that is broadly applicable, adaptable, and effective across a wide range of problem settings characterized by objective functions that are expressed in terms of graph neighbourhoods with interdependent and related variables. Extensive experimentation has been conducted to compare and evaluate the proposed crossover operator with both classic and specialized crossover operators. More specifically, the crossover operators have been tested under a variety of graph types, which model how variables are involved in interdependencies, different types of expressions in which interdependent variables are combined, and different numbers of decision variables. The results suggest that the new crossover operator is statistically better or at least as good as the best-performing crossover in 75% of the families of problems tested. |
| format | Article |
| id | doaj-art-87c7e8ad7c604c568654013719d0598d |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-87c7e8ad7c604c568654013719d0598d2025-02-09T13:01:18ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111212310.1007/s40747-024-01721-8A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variablesJaume Jordan0Javier Palanca1Victor Sanchez-Anguix2Vicente Julian3Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de ValènciaValencian Research Institute for Artificial Intelligence, Universitat Politècnica de ValènciaInstituto Tecnológico de Informática, Universitat Politècnica de ValènciaValencian Research Institute for Artificial Intelligence, Universitat Politècnica de ValènciaAbstract This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions, one for each neighborhood. The main goal of this work is to propose a crossover operator that is broadly applicable, adaptable, and effective across a wide range of problem settings characterized by objective functions that are expressed in terms of graph neighbourhoods with interdependent and related variables. Extensive experimentation has been conducted to compare and evaluate the proposed crossover operator with both classic and specialized crossover operators. More specifically, the crossover operators have been tested under a variety of graph types, which model how variables are involved in interdependencies, different types of expressions in which interdependent variables are combined, and different numbers of decision variables. The results suggest that the new crossover operator is statistically better or at least as good as the best-performing crossover in 75% of the families of problems tested.https://doi.org/10.1007/s40747-024-01721-8Genetic algorithmsOptimizationArtificial intelligenceMetaheuristics |
| spellingShingle | Jaume Jordan Javier Palanca Victor Sanchez-Anguix Vicente Julian A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables Complex & Intelligent Systems Genetic algorithms Optimization Artificial intelligence Metaheuristics |
| title | A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| title_full | A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| title_fullStr | A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| title_full_unstemmed | A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| title_short | A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| title_sort | crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables |
| topic | Genetic algorithms Optimization Artificial intelligence Metaheuristics |
| url | https://doi.org/10.1007/s40747-024-01721-8 |
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