Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization
Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of popul...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9159113/ |
| _version_ | 1818930184005353472 |
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| author | Rodrigo Lisboa Pereira Daniel Leal Souza Marco Antonio Florenzano Mollinetti Mario T. R. Serra Neto Edson Koiti Kudo Yasojima Otavio Noura Teixeira Roberto Celio Limao De Oliveira |
| author_facet | Rodrigo Lisboa Pereira Daniel Leal Souza Marco Antonio Florenzano Mollinetti Mario T. R. Serra Neto Edson Koiti Kudo Yasojima Otavio Noura Teixeira Roberto Celio Limao De Oliveira |
| author_sort | Rodrigo Lisboa Pereira |
| collection | DOAJ |
| description | Game Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA. |
| first_indexed | 2024-12-20T03:56:39Z |
| format | Article |
| id | doaj.art-f7f41581cb604c658968c6c1a3eb6e46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:56:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f7f41581cb604c658968c6c1a3eb6e462022-12-21T19:54:18ZengIEEEIEEE Access2169-35362020-01-01814483914486510.1109/ACCESS.2020.30145779159113Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained OptimizationRodrigo Lisboa Pereira0https://orcid.org/0000-0001-5217-908XDaniel Leal Souza1https://orcid.org/0000-0002-1345-6723Marco Antonio Florenzano Mollinetti2https://orcid.org/0000-0002-5948-691XMario T. R. Serra Neto3https://orcid.org/0000-0002-4682-7789Edson Koiti Kudo Yasojima4https://orcid.org/0000-0003-2305-8873Otavio Noura Teixeira5https://orcid.org/0000-0002-7860-5996Roberto Celio Limao De Oliveira6https://orcid.org/0000-0002-6640-3182Post Graduation Program in Electrical Engineering, Federal University of Pará (UFPA), Belém, BrazilPost Graduation Program in Electrical Engineering, Federal University of Pará (UFPA), Belém, BrazilComputational Technologies Laboratory (LabTeC), Federal Rural University of Amazonia (UFRA), Paragominas, BrazilComputational Technologies Laboratory (LabTeC), Federal Rural University of Amazonia (UFRA), Paragominas, BrazilComputational Technologies Laboratory (LabTeC), Federal Rural University of Amazonia (UFRA), Paragominas, BrazilComputational Technologies Laboratory (LabTeC), Federal Rural University of Amazonia (UFRA), Paragominas, BrazilPost Graduation Program in Electrical Engineering, Federal University of Pará (UFPA), Belém, BrazilGame Theory (GT) formalizes dispute scenarios between two or more players where each one makes a move following their strategy profiles. The following paper introduces the integration of GT to selection and crossover steps of Genetic Algorithms as an evolutionary model of the representation of population in a similar way to human social evolution. Two ideas are proposed to be incorporated into the GA. First, the Genetic Algorithm with Social Interaction (GASI), a family of GAs that uses GT in selection phase to increase the diversification of the solutions. Second, the (Game-Based Crossover) GBX and GBX2 crossover operators, competition-based tournament selection methods that employ social dispute to generate more diverse offspring. Performance and robustness of the new approaches were assessed by ten continuous and constrained engineering design optimization problems and compared against variants of the canonical GA, as well as well-known heuristics from the literature. Results indicate significant performance relevance in most instances compared to other algorithms and highlight the benefits of combining GT and GA.https://ieeexplore.ieee.org/document/9159113/Genetic algorithmsgame theoryconstrained optimizationselective pressure |
| spellingShingle | Rodrigo Lisboa Pereira Daniel Leal Souza Marco Antonio Florenzano Mollinetti Mario T. R. Serra Neto Edson Koiti Kudo Yasojima Otavio Noura Teixeira Roberto Celio Limao De Oliveira Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization IEEE Access Genetic algorithms game theory constrained optimization selective pressure |
| title | Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization |
| title_full | Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization |
| title_fullStr | Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization |
| title_full_unstemmed | Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization |
| title_short | Game Theory and Social Interaction for Selection and Crossover Pressure Control in Genetic Algorithms: An Empirical Analysis to Real-Valued Constrained Optimization |
| title_sort | game theory and social interaction for selection and crossover pressure control in genetic algorithms an empirical analysis to real valued constrained optimization |
| topic | Genetic algorithms game theory constrained optimization selective pressure |
| url | https://ieeexplore.ieee.org/document/9159113/ |
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