New Insights into Fuzzy Genetic Algorithms for Optimization Problems
In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to genera...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/12/549 |
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| author | Oleksandr Syzonov Stefania Tomasiello Nicola Capuano |
| author_facet | Oleksandr Syzonov Stefania Tomasiello Nicola Capuano |
| author_sort | Oleksandr Syzonov |
| collection | DOAJ |
| description | In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child’s fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. |
| format | Article |
| id | doaj-art-a2ce6b8875fb4ce4a707783a383c27d7 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-a2ce6b8875fb4ce4a707783a383c27d72024-12-27T14:05:12ZengMDPI AGAlgorithms1999-48932024-12-01171254910.3390/a17120549New Insights into Fuzzy Genetic Algorithms for Optimization ProblemsOleksandr Syzonov0Stefania Tomasiello1Nicola Capuano2Institute of Computer Science, University of Tartu, 50090 Tartu, EstoniaInstitute of Computer Science, University of Tartu, 50090 Tartu, EstoniaDepartment of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, ItalyIn this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child’s fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy.https://www.mdpi.com/1999-4893/17/12/549nearest neighborcachingfuzzy rulesfitnesspriority |
| spellingShingle | Oleksandr Syzonov Stefania Tomasiello Nicola Capuano New Insights into Fuzzy Genetic Algorithms for Optimization Problems Algorithms nearest neighbor caching fuzzy rules fitness priority |
| title | New Insights into Fuzzy Genetic Algorithms for Optimization Problems |
| title_full | New Insights into Fuzzy Genetic Algorithms for Optimization Problems |
| title_fullStr | New Insights into Fuzzy Genetic Algorithms for Optimization Problems |
| title_full_unstemmed | New Insights into Fuzzy Genetic Algorithms for Optimization Problems |
| title_short | New Insights into Fuzzy Genetic Algorithms for Optimization Problems |
| title_sort | new insights into fuzzy genetic algorithms for optimization problems |
| topic | nearest neighbor caching fuzzy rules fitness priority |
| url | https://www.mdpi.com/1999-4893/17/12/549 |
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