Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming
Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP varia...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/1336929 |
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| author | Libin Hong Chenjian Liu Jiadong Cui Fuchang Liu |
| author_facet | Libin Hong Chenjian Liu Jiadong Cui Fuchang Liu |
| author_sort | Libin Hong |
| collection | DOAJ |
| description | Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested. |
| format | Article |
| id | doaj-art-dd23ff63f8e74657ae3a3dca3634c8dd |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-dd23ff63f8e74657ae3a3dca3634c8dd2025-08-20T02:21:33ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/13369291336929Mutation Strategy Based on Step Size and Survival Rate for Evolutionary ProgrammingLibin Hong0Chenjian Liu1Jiadong Cui2Fuchang Liu3School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaCollege of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaEvolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.http://dx.doi.org/10.1155/2021/1336929 |
| spellingShingle | Libin Hong Chenjian Liu Jiadong Cui Fuchang Liu Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming Discrete Dynamics in Nature and Society |
| title | Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming |
| title_full | Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming |
| title_fullStr | Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming |
| title_full_unstemmed | Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming |
| title_short | Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming |
| title_sort | mutation strategy based on step size and survival rate for evolutionary programming |
| url | http://dx.doi.org/10.1155/2021/1336929 |
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