Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems
The article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub-optimal solutions. Genetic Algorithm and Evoluti...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-06-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1730631 |
| _version_ | 1797684868207869952 |
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| author | Krzysztof Pytel |
| author_facet | Krzysztof Pytel |
| author_sort | Krzysztof Pytel |
| collection | DOAJ |
| description | The article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub-optimal solutions. Genetic Algorithm and Evolutionary Strategy can periodically exchange the best individuals from each other. The algorithm combines the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. It maintains the right balance between the exploration and exploitation of the search space. The results of the experiments suggest that the proposed algorithm is more effective than the Genetic Algorithms and Evolutionary Strategy used separately, and can be an effective tool in solving complex optimization problems. |
| first_indexed | 2024-03-12T00:35:58Z |
| format | Article |
| id | doaj.art-3a14367ca25f4123941b7f2ed2c89e6e |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:35:58Z |
| publishDate | 2020-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-3a14367ca25f4123941b7f2ed2c89e6e2023-09-15T09:33:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452020-06-0134755056310.1080/08839514.2020.17306311730631Hybrid Multi-Evolutionary Algorithm to Solve Optimization ProblemsKrzysztof Pytel0University of LodzThe article presents a Hybrid Multi-Evolutionary Algorithm designed to solve optimization problems. The Genetic Algorithm and Evolutionary Strategy work together to improve the efficiency of optimization and increase resistance to getting stuck to sub-optimal solutions. Genetic Algorithm and Evolutionary Strategy can periodically exchange the best individuals from each other. The algorithm combines the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. It maintains the right balance between the exploration and exploitation of the search space. The results of the experiments suggest that the proposed algorithm is more effective than the Genetic Algorithms and Evolutionary Strategy used separately, and can be an effective tool in solving complex optimization problems.http://dx.doi.org/10.1080/08839514.2020.1730631 |
| spellingShingle | Krzysztof Pytel Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems Applied Artificial Intelligence |
| title | Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems |
| title_full | Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems |
| title_fullStr | Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems |
| title_full_unstemmed | Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems |
| title_short | Hybrid Multi-Evolutionary Algorithm to Solve Optimization Problems |
| title_sort | hybrid multi evolutionary algorithm to solve optimization problems |
| url | http://dx.doi.org/10.1080/08839514.2020.1730631 |
| work_keys_str_mv | AT krzysztofpytel hybridmultievolutionaryalgorithmtosolveoptimizationproblems |