A Framework for Accelerating Metaheuristics via Pattern Reduction

Chun Wei Tsai, Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper presents a novel framework based on the notion of pattern reduction, called Framework for Accelerating Metaheuristics via Pattern Reduction (FAMPR), to solve an intrinsic problem of metaheuristics. That is, many computations of metaheuristics during the convergence process are essentially redundant. As such, if they can be eliminated, the computation time of metaheuristics can be significantly reduced. Our experimental result shows that the proposed framework can significantly reduce the computation time of metaheuristics while limiting the loss of quality to a very small percentage.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
Pages293-294
Number of pages2
DOIs
Publication statusPublished - 2010 Aug 27
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: 2010 Jul 72010 Jul 11

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Other

Other12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
CountryUnited States
CityPortland, OR
Period10-07-0710-07-11

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All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Tsai, C. W., Tseng, S. P., Chiang, M. C., & Yang, C. S. (2010). A Framework for Accelerating Metaheuristics via Pattern Reduction. In Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 (pp. 293-294). (Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10). https://doi.org/10.1145/1830483.1830537