An improved genetic algorithm with average-bound crossover and wavelet mutation operations

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84 Citations (Scopus)

Abstract

This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.
Original languageEnglish
Pages (from-to)7-31
Number of pages25
JournalSoft Computing
Volume11
Issue number1
DOIs
Publication statusPublished - 1 Jan 2007

Keywords

  • Associative-memory neural network
  • Crossover
  • Economic load dispatch
  • Mutation
  • Real-coded genetic algorithm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

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