Generating combinations on the GPU and its application to the k-subset sum

研究成果: Conference contribution

抄録

Efficiently representing and generating combinations can allow the seamless visualization, sampling, and evaluation of combinatorial architectures. In this paper, being relevant to tackle resource allocation problems ubiquitously, we address the subset sum problem by (1) using gradient-free optimization with a number-based representation of the combinatorial search space and by (2) generating combinations with minimal change order through parallel reductions in the GPU. Our computational experiments consisting of a relevant set of problem instances and gradient-free optimization algorithms show that (1) it is possible to generate combinations in the GPU efficiently, with quasi-linear complexity, (2) it is possible to tackle instances of the subset sum problem within a reasonable number of function evaluations, and (3) Particle Swarm Optimization with Fitness Euclidean Ratio converges faster. Since the search space of number-based representations is one-dimensional and amenable to parallelization schemes (e.g., GPU), we believe our work opens the door to tackle further combinatorial problems.

本文言語English
ホスト出版物のタイトルGECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ1308-1316
ページ数9
ISBN(電子版)9781450383516
DOI
出版ステータスPublished - 2021 7 7
イベント2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
継続期間: 2021 7 102021 7 14

出版物シリーズ

名前GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
国/地域France
CityVirtual, Online
Period21/7/1021/7/14

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 計算理論と計算数学

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