Self-adaptive partially mapped crossover

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Self-adaptive partially mapped crossover

Full text

Pdf (73 KB)

Source

Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents

London, England

POSTER SESSION: Genetic algorithms: posters table of contents

Pages: 1523 - 1523

Year of Publication: 2007

ISBN:978-1-59593-697-4

Authors

Oliver Kramer	University of Dortmund, Dortmund, Germany
Patrick Koch	University of Paderborn, Paderborn, Germany

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 10, Downloads (12 Months): 55, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1276958.1277252 What is a DOI?

ABSTRACT

Self-adaptive crossover is a step towards exploiting the structure of problems automatically by evolution. We present a self-adaptive extension of the partially mapped crossover (PMX) operator that controls the crossover points. Because the link between strategy parameters and fitness is weak for self-adaptive crossover, superior results were hard to gather in the past. We can now report encouraging experimental results for the PMX on the traveling salesman problem (TSP) as an example for combinatorial problems.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	D. E. Goldberg and R. Lingle. Alleles, loci and the traveling salesman problem. pages 154--159, 1985.
	2	S. Meyer-Nieberg and H.-G. Beyer. Self-adaptation in evolutionary algorithms. Springer, Berlin, 2007.