GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems

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Ohira, Ryoma
Islam, Md Saiful
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2020
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Glasgow, United Kingdom
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Abstract

The island model allows genetic algorithms to effectively maintain diversity through migration between multiple independent populations. Due to its flexibility and modularity, it is commonly employed in distributed and parallel implementations, particularly in recent trends in leveraging the massively parallel cores in GPUs. However, the efficiency and effectiveness of the island model can be considered as its ability to manage its global and local search while minimising the overlap of islands searching in the same area of the solution space. This paper introduces a GPU accelerated island-model genetic algorithm that conducts global search by organising its populations into islands according to the similarity in genotype sequences. Local search is managed through adaptive mechanisms designed to maintain population diversity. The characteristics of the proposed genetic algorithm are investigated with encouraging results demonstrating its robustness and scalability when solving ordered optimisation problems.

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2020 IEEE Congress on Evolutionary Computation (CEC 2020)
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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Artificial intelligence not elsewhere classified
Optimisation
Computational complexity and computability
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Ohira, R; Islam, MS, GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems, 2020 IEEE Congress on Evolutionary Computation (CEC 2020), 2020