The automatic design of parameter adaptation techniques for differential evolution with genetic programming
This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization proc...
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| Vydáno v: | Knowledge-based systems Ročník 239; s. 108070 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
05.03.2022
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization process of differential evolution based on the current computational resource, ratio of successful solutions and adapted scaling factor value. The design of the parameter adaptation technique is performed by a computational experiment, which consisted in solving several complex optimization problems. Better symbolic expressions are selected with regards to the Friedman ranking procedure, and the best solutions are additionally evaluated to compare them to the existing parameter adaptation techniques. The experimental results show that the automatically designed parameter adaptation techniques described by symbolic expressions are capable of outperforming existing parameter adaptation methods, while using different information sources. The analysis of automatically generated solutions shows that the proposed technique can be considered an automatic knowledge extraction method. This is due to the results showing that well-performing parameter adaptation can behave differently from state-of-the-art methods, thereby revealing previously unknown algorithm properties. |
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| AbstractList | This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization process of differential evolution based on the current computational resource, ratio of successful solutions and adapted scaling factor value. The design of the parameter adaptation technique is performed by a computational experiment, which consisted in solving several complex optimization problems. Better symbolic expressions are selected with regards to the Friedman ranking procedure, and the best solutions are additionally evaluated to compare them to the existing parameter adaptation techniques. The experimental results show that the automatically designed parameter adaptation techniques described by symbolic expressions are capable of outperforming existing parameter adaptation methods, while using different information sources. The analysis of automatically generated solutions shows that the proposed technique can be considered an automatic knowledge extraction method. This is due to the results showing that well-performing parameter adaptation can behave differently from state-of-the-art methods, thereby revealing previously unknown algorithm properties. |
| ArticleNumber | 108070 |
| Author | Stanovov, Vladimir Semenkin, Eugene Akhmedova, Shakhnaz |
| Author_xml | – sequence: 1 givenname: Vladimir orcidid: 0000-0002-1695-5798 surname: Stanovov fullname: Stanovov, Vladimir email: vladimirstanovov@yandex.ru – sequence: 2 givenname: Shakhnaz orcidid: 0000-0003-2927-1974 surname: Akhmedova fullname: Akhmedova, Shakhnaz email: shahnaz@inbox.ru – sequence: 3 givenname: Eugene surname: Semenkin fullname: Semenkin, Eugene email: eugenesemenkin@yandex.ru |
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| Keywords | Differential evolution Parameter adaptation Genetic programming |
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| SubjectTerms | Adaptation Algorithms Automatic Design factors Design parameters Differential evolution Evolution Evolutionary algorithms Evolutionary computation Extraction Genetic algorithms Genetic programming Genetics Information sources Optimization Parameter adaptation Programming Property Scaling factors Symbolism Symbols |
| Title | The automatic design of parameter adaptation techniques for differential evolution with genetic programming |
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