An effective uniform genetic algorithm for hard optimization problems

Yuping Wang*, Hailin Liu, Yiu Wing Leung

*Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

6 Citations (Scopus)

Abstract

Genetic algorithms are one of the effective algorithms for hard optimization problems. They can escape from the local minima, however, the amount of their computation is often large. To decrease the amount of the computation and enhance the algorithms, the uniform design is combined into the genetic algorithm. The new genetic operator has the local-search property similar to that in traditional optimization techniques and needs a minimal amount of computation in certain meaning. Thus the new genetic algorithm can generate a diversity of population and explore the search space effectively. Moreover, the new algorithm is globally convergent. The numerical results also show the effectiveness of the new algorithm with its less computation, higher convergent speed for all test functions.

Original languageEnglish
Pages656-660
Number of pages5
Publication statusPublished - 2000
EventProceedings of the 3th World Congress on Intelligent Control and Automation - Hefei, China
Duration: 28 Jun 20002 Jul 2000

Conference

ConferenceProceedings of the 3th World Congress on Intelligent Control and Automation
Country/TerritoryChina
CityHefei
Period28/06/002/07/00

Scopus Subject Areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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