A novel population initialization method for accelerating evolutionary algorithms

Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M.A. Salama

Research output: Contribution to journalArticlepeer-review


Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposition-based learning to generate initial population. The conducted experiments over a comprehensive set of benchmark functions demonstrate that replacing the random initialization with the opposition-based population initialization can accelerate convergence speed.

Original languageEnglish (US)
Pages (from-to)1605-1614
Number of pages10
JournalComputers and Mathematics with Applications
Issue number10
StatePublished - May 2007


  • Differential evolution (DE)
  • Evolutionary algorithms
  • Global optimization
  • Opposition-based learning
  • Random initialization

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics


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