skip to main content
10.1145/1276958.1277245acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Adaptive markov recombination

Published: 07 July 2007 Publication History

Abstract

A new class of 'crossover' algorithms is proposed that isused in what we coin the Genetic Engineering strategy. These algorithms explicitly consider the fitness of all subsets of candidate solutions when creating the next iteration of candidate solutions. If the fitness of individual solutions is positively correlated to the fitness of their subsets, one could optimize faster by decreasing the probability that good subsets are destroyed while performing recombination. Finding promising partial solutions turns out to be simply a matter of counting. Our implementation, Markov recombination, creates a histogram of all symbol transitions in all candidate solutions in the population at a certain time step. Randomized Markov chains is then be used to generate offspring.

References

[1]
J. H. Holland. Adaptation in natural and artificial systems. 1975.
[2]
D. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Trans. Evolutionary Computation, 1(1):67--82, 1997.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. adaptation/self-adaptation
  2. evolution strategies
  3. genetic algorithms
  4. recombination operators

Qualifiers

  • Article

Conference

GECCO07
Sponsor:

Acceptance Rates

GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 80
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media