Calibrating strategies for evolutionary algorithms

Elizabeth Montero, María Cristina Riff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Number of pages6
Publication statusPublished - 1 Dec 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sept 200728 Sept 2007


Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science


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