Dubey, Rahul orcid.org/0000-0003-1524-7797, Hickinbotham, Simon John orcid.org/0000-0003-0880-4460, Price, Mark et al. (1 more author) (2023) Local Fitness Landscape Exploration Based Genetic Algorithms. IEEE Access. pp. 3324-3337. ISSN 2169-3536
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Genetic algorithms,fitness landscape approximation,Multi-objective optimization,evolutionary search |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/V007335/1 |
Depositing User: | Pure (York) |
Date Deposited: | 04 Jan 2023 10:20 |
Last Modified: | 17 Dec 2024 00:25 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194746 |