Neuroevolution for Parameter Adaptation in Differential Evolution
Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/4/122 |
| _version_ | 1827622039637196800 |
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| author | Vladimir Stanovov Shakhnaz Akhmedova Eugene Semenkin |
| author_facet | Vladimir Stanovov Shakhnaz Akhmedova Eugene Semenkin |
| author_sort | Vladimir Stanovov |
| collection | DOAJ |
| description | Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in this study are used for setting the scaling factor and crossover rate values based on the available information about the algorithm performance and previous successful values. The training is performed on a set of benchmark problems, and the testing and comparison is performed on several different benchmarks to evaluate the generalizing ability of the approach. The neuroevolution is enhanced with lexicase selection to handle the noisy fitness landscape of the benchmarking results. The experimental results show that it is possible to design efficient parameter adaptation techniques comparable to state-of-the-art methods, although such an automatic search for heuristics requires significant computational effort. The automatically designed solutions can be further analyzed to extract valuable knowledge about parameter adaptation. |
| first_indexed | 2024-03-09T11:16:39Z |
| format | Article |
| id | doaj.art-2a942d2f10e54f98949c1b6636141c49 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T11:16:39Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-2a942d2f10e54f98949c1b6636141c492023-12-01T00:28:55ZengMDPI AGAlgorithms1999-48932022-04-0115412210.3390/a15040122Neuroevolution for Parameter Adaptation in Differential EvolutionVladimir Stanovov0Shakhnaz Akhmedova1Eugene Semenkin2Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaParameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in this study are used for setting the scaling factor and crossover rate values based on the available information about the algorithm performance and previous successful values. The training is performed on a set of benchmark problems, and the testing and comparison is performed on several different benchmarks to evaluate the generalizing ability of the approach. The neuroevolution is enhanced with lexicase selection to handle the noisy fitness landscape of the benchmarking results. The experimental results show that it is possible to design efficient parameter adaptation techniques comparable to state-of-the-art methods, although such an automatic search for heuristics requires significant computational effort. The automatically designed solutions can be further analyzed to extract valuable knowledge about parameter adaptation.https://www.mdpi.com/1999-4893/15/4/122differential evolutionneuroevolutionparameter adaptationneuroevolution of augmented topologies |
| spellingShingle | Vladimir Stanovov Shakhnaz Akhmedova Eugene Semenkin Neuroevolution for Parameter Adaptation in Differential Evolution Algorithms differential evolution neuroevolution parameter adaptation neuroevolution of augmented topologies |
| title | Neuroevolution for Parameter Adaptation in Differential Evolution |
| title_full | Neuroevolution for Parameter Adaptation in Differential Evolution |
| title_fullStr | Neuroevolution for Parameter Adaptation in Differential Evolution |
| title_full_unstemmed | Neuroevolution for Parameter Adaptation in Differential Evolution |
| title_short | Neuroevolution for Parameter Adaptation in Differential Evolution |
| title_sort | neuroevolution for parameter adaptation in differential evolution |
| topic | differential evolution neuroevolution parameter adaptation neuroevolution of augmented topologies |
| url | https://www.mdpi.com/1999-4893/15/4/122 |
| work_keys_str_mv | AT vladimirstanovov neuroevolutionforparameteradaptationindifferentialevolution AT shakhnazakhmedova neuroevolutionforparameteradaptationindifferentialevolution AT eugenesemenkin neuroevolutionforparameteradaptationindifferentialevolution |