Improved analysis methods for crossover-based algorithms

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

Abstract

We deepen the theoretical analysis of the genetic algorithm for the all-pairs shortest path problem proposed by Doerr, Happ and Klein (GECCO 2008). We show that the growth of the paths through crossover operations can be analyzed without the previously used approach of waiting until all paths of a certain length are present in the population. This allows to prove an improved guarantee for the optimization time of O(n3.25 log1/4(n)). We also show that this bound is asymptotically tight. Besides the mere run-time result, our analysis is a step towards understanding how crossover works and how it can be analyzed with rigorous methods.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Pages247-253
Number of pages7
DOIs
Publication statusPublished - 31 Dec 2009
Externally publishedYes
Event11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada
Duration: 8 Jul 200912 Jul 2009

Publication series

NameProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Conference

Conference11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Country/TerritoryCanada
CityMontreal, QC
Period8/07/0912/07/09

Keywords

  • Combinatorial optimization
  • Crossover
  • Evolutionary algorithm

Fingerprint

Dive into the research topics of 'Improved analysis methods for crossover-based algorithms'. Together they form a unique fingerprint.

Cite this