On-the-fly calibrating strategies for evolutionary algorithms

Elizabeth Montero, María Cristina Riff

Research output: Contribution to journalArticle

19 Citations (Scopus)


The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning. These strategies provide efficient and low-cost adaptive techniques for parameter control and they preserve the original design of the evolutionary algorithm, as they can be included without changing either the structure of the algorithm nor its operators design.

Original languageEnglish
Pages (from-to)552-566
Number of pages15
JournalInformation Sciences
Issue number3
Publication statusPublished - 1 Feb 2011


  • Evolutionary algorithms
  • Parameter control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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