Fitness-levels for non-elitist populations

Per Kristian Lehre*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Citations (Scopus)


This paper introduces an easy to use technique for deriving upper bounds on the expected runtime of non-elitist population-based evolutionary algorithms (EAs). Applications of the technique show how the efficiency of EAs is critically dependant on having a sufficiently strong selective pressure. Parameter settings that ensure sufficient selective pressure on commonly considered benchmark functions are derived for the most popular selection mechanisms. Together with a recent technique for deriving lower bounds, this paper contributes to a much-needed analytical tool-box for the analysis of evolutionary algorithms with populations.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Number of pages8
Publication statusPublished - 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011


Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11


  • Evolutionary algorithms
  • Runtime analysis

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science


Dive into the research topics of 'Fitness-levels for non-elitist populations'. Together they form a unique fingerprint.

Cite this