ACO beats EA on a dynamic pseudo-boolean function

Timo Kötzing, Hendrik Molter

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

39 Scopus citations


In this paper, we contribute to the understanding of the behavior of bio-inspired algorithms when tracking the optimum of a dynamically changing fitness function over time. In particular, we are interested in the difference between a simple evolutionary algorithm (EA) and a simple ant colony optimization (ACO) system on deterministically changing fitness functions, which we call dynamic fitness patterns. Of course, the algorithms have no prior knowledge about the patterns. We construct a bit string optimization problem where we can show that the ACO system is able to follow the optimum while the EA gets lost.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
Number of pages10
EditionPART 1
StatePublished - 24 Sep 2012
Externally publishedYes
Event12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy
Duration: 1 Sep 20125 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7491 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Parallel Problem Solving from Nature, PPSN 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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