A variance decomposition approach to the analysis of genetic algorithms Conference Paper


Author(s): Paixão, Tiago; Barton, Nicholas H
Title: A variance decomposition approach to the analysis of genetic algorithms
Affiliation IST Austria
Abstract: Prediction of the evolutionary process is a long standing problem both in the theory of evolutionary biology and evolutionary computation (EC). It has long been realized that heritable variation is crucial to both the response to selection and the success of genetic algorithms. However, not all variation contributes in the same way to the response. Quantitative genetics has developed a large body of work trying to estimate and understand how different components of the variance in fitness in the population contribute to the response to selection. We illustrate how to apply some concepts of quantitative genetics to the analysis of genetic algorithms. In particular, we derive estimates for the short term prediction of the response to selection and we use variance decomposition to gain insight on local aspects of the landscape. Finally, we propose a new population based genetic algorithm that uses these methods to improve its operation.
Conference Title: GECCO: Genetic and evolutionary computation conference
Conference Dates: July 6-10, 2013
Conference Location: Amsterdam, Netherlands
ISBN: 978-1-4503-1963-8
Publisher: ACM  
Date Published: 2013-07-01
Start Page: 845
End Page: 852
Sponsor: This work was supported by ERC Advanced Grant ERC-2009-AdG-250152 SELECTIONINFORMATION.
DOI: 10.1145/2463372.2463470
Notes: The authors would like to thank five anonymous reviewers for their helpful comments.
Open access: no
IST Austria Authors
  1. Nick Barton
    252 Barton
  2. Tiago Paixão
    27 Paixão