Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search
The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conduc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/7/982 |
| _version_ | 1797439861855092736 |
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| author | Emad Mabrouk Yara Raslan Abdel-Rahman Hedar |
| author_facet | Emad Mabrouk Yara Raslan Abdel-Rahman Hedar |
| author_sort | Emad Mabrouk |
| collection | DOAJ |
| description | The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems. |
| first_indexed | 2024-03-09T11:58:48Z |
| format | Article |
| id | doaj.art-abd738e02b8547c8be95071accfae6fe |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T11:58:48Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-abd738e02b8547c8be95071accfae6fe2023-11-30T23:05:46ZengMDPI AGElectronics2079-92922022-03-0111798210.3390/electronics11070982Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local SearchEmad Mabrouk0Yara Raslan1Abdel-Rahman Hedar2College of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitDepartment of Mathematics, Faculty of Science, Assiut University, Assiut 71516, EgyptDepartment of Computer Science, Faculty of Computers & Information, Assiut University, Assiut 71526, EgyptThe foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems.https://www.mdpi.com/2079-9292/11/7/982artificial immune systemimmune system programmingmachine learningmeta-heuristicsmeta-heuristic programming |
| spellingShingle | Emad Mabrouk Yara Raslan Abdel-Rahman Hedar Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search Electronics artificial immune system immune system programming machine learning meta-heuristics meta-heuristic programming |
| title | Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search |
| title_full | Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search |
| title_fullStr | Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search |
| title_full_unstemmed | Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search |
| title_short | Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search |
| title_sort | immune system programming a machine learning approach based on artificial immune systems enhanced by local search |
| topic | artificial immune system immune system programming machine learning meta-heuristics meta-heuristic programming |
| url | https://www.mdpi.com/2079-9292/11/7/982 |
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