Continuous space pattern reduction for genetic clustering algorithm

Chun Wei Tsai, Tzu Yuan Lin, Ming Chao Chiang, Chu-Sing Yang, Tzung Pei Hong

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

2 Citations (Scopus)

Abstract

We have recently proposed a highly effective method for speeding up metaheuristics in solving combinatorial optimization problems called pattern reduction (PR). It is, however, limited to problems with solutions that are either binary or integer encoded. In this paper, we proposed a new pattern reduction algorithm named continuous space pattern reduction (CSPR) to overcome this limitation. Simulations show that the proposed algorithm can significantly reduce the computation time of k-means with genetic algorithm (KGA) for solving the data clustering problem using continuous encoding. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
Pages1475-1476
Number of pages2
DOIs
Publication statusPublished - 2012 Aug 20
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 2012 Jul 72012 Jul 11

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Other

Other14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period12-07-0712-07-11

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics

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