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Parallel Genetic Algorithms, Parameters and Design

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Artificial Intelligence and Smart Environment (ICAISE 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 635))

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Genetic Algorithms are widely used in the quest of optimization of real-world complex problems. Parallel Genetic Algorithms may be considered as an evolution of the traditional GA. However, they are designed differently. Designing a Parallel Genetic Algorithm depends on many parameters other than the selection, the crossover, and the mutation parameters. In the literature, we can run into multiple and confusing Parallel Genetic Algorithms model names. These models are a sort of combination of specific parameters. Some of these parameters are the number of populations, the granularity of each population, the migration operation, the overlapping operation. This article lists these parameters used to design Parallel Genetic Algorithms so that the practitioner can design his own model, knowing the meaning and the use of each parameter.

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Correspondence to Mustapha Ouiss .

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Ouiss, M., Ettaoufik, A., Marzak, A., Tragha, A. (2023). Parallel Genetic Algorithms, Parameters and Design. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol 635. Springer, Cham.

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