Abstract
Large Scale Global Optimization (LSGO) is an identified problem in the literature and almost all Bio-inspired metaheuristic search-based optimization algorithms, including Genetic Algorithm (GA), face this problem. The article first identifies schema deception, domino convergence with genetic drift and nonseparability among the variables as the reasons for LSGO. Towards LSGO solution, the article progresses the cooperative coevolution approach, with some novel concepts in solution representation, subcomponent selection, search mechanism and static adaptation of algorithm parameters. The concepts are demonstrated on GA as it is having a strong theoretical and mathematical background. Many GA variants are derived based on the above novel concepts those better balance the exploration and exploitation abilities of an algorithm, specifically at large scale. The proposal is justified using a performance comparison with other algorithms on some of the LSGO test bench functions with 4–20 dimensions.
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Choithram Dharmani, B. (2023). Gradual Search and Fixed Grouping Scheme Based Variant of Genetic Algorithm for Large Scale Global Optimization. In: Thampi, S.M., Mukhopadhyay, J., Paprzycki, M., Li, KC. (eds) International Symposium on Intelligent Informatics. ISI 2022. Smart Innovation, Systems and Technologies, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-19-8094-7_7
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