Local Fitness Landscape Exploration Based Genetic Algorithms
Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10007811/ |
| _version_ | 1828011382074769408 |
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| author | Rahul Dubey Simon Hickinbotham Mark Price Andy Tyrrell |
| author_facet | Rahul Dubey Simon Hickinbotham Mark Price Andy Tyrrell |
| author_sort | Rahul Dubey |
| collection | DOAJ |
| description | Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems. |
| first_indexed | 2024-04-10T09:14:30Z |
| format | Article |
| id | doaj.art-942f3ff54f4547cdaea9110c3682a517 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-942f3ff54f4547cdaea9110c3682a5172023-02-21T00:01:37ZengIEEEIEEE Access2169-35362023-01-01113324333710.1109/ACCESS.2023.323477510007811Local Fitness Landscape Exploration Based Genetic AlgorithmsRahul Dubey0https://orcid.org/0000-0003-1524-7797Simon Hickinbotham1Mark Price2https://orcid.org/0000-0002-4551-4457Andy Tyrrell3https://orcid.org/0000-0002-8533-2404Department of Electronics Engineering, University of York, York, U.K.Department of Electronics Engineering, University of York, York, U.K.School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast, U.K.Department of Electronics Engineering, University of York, York, U.K.Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.https://ieeexplore.ieee.org/document/10007811/Genetic algorithmsfitness landscape approximationmulti-objective optimizationevolutionary search |
| spellingShingle | Rahul Dubey Simon Hickinbotham Mark Price Andy Tyrrell Local Fitness Landscape Exploration Based Genetic Algorithms IEEE Access Genetic algorithms fitness landscape approximation multi-objective optimization evolutionary search |
| title | Local Fitness Landscape Exploration Based Genetic Algorithms |
| title_full | Local Fitness Landscape Exploration Based Genetic Algorithms |
| title_fullStr | Local Fitness Landscape Exploration Based Genetic Algorithms |
| title_full_unstemmed | Local Fitness Landscape Exploration Based Genetic Algorithms |
| title_short | Local Fitness Landscape Exploration Based Genetic Algorithms |
| title_sort | local fitness landscape exploration based genetic algorithms |
| topic | Genetic algorithms fitness landscape approximation multi-objective optimization evolutionary search |
| url | https://ieeexplore.ieee.org/document/10007811/ |
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