Definition of a crossover based distance for genetic algorithms
Pages 1473 - 1474
Abstract
Distances that are bound to (or consistent with) genetic operators are measures that quantify the difficulty of reaching and individual (or a population) starting from another individual (or population) and applying the genetic operator iteratively. Defining distance measures bound to genetic operators is a very important task in evolutionary computation. In fact these distances usually make the analysis of some indicators of the the search process, like for instance population diversity or well-known measures of problem hardness such as fitness distance correlation, more accurate. In this paper, we introduce a distance measure bound to one point standard crossover for genetic algorithms. This measure quantifies the minimum number of crossover operations that have to be applied to a population to tranform it into another population. It is based on the definition of a lattice over some particular schemata that represent the individuals in the population and on the construction of a discrete dynamic system that models the dynamics of the genetic algorithm under the sole effect of crossover. Using this distance measure, it is also possible to build a family of distances between individuals.
References
[1]
T. S. Blyth. Lattices and ordered algebraic structures. Springer-Verlag London, 2005.
[2]
A. Ekárt and S. Z. Németh. Maintaining the diversity of genetic programs. In J. A. Foster, E. Lutton, J. Miller, C. Ryan, and A. G. B. Tettamanzi, editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 162--171, Kinsale, Ireland, 3-5 Apr. 2002. Springer, Berlin, Heidelberg, New York.
[3]
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
[4]
S. Gustafson. An Analysis of Diversity in Genetic Programming. PhD thesis, School of Computer Science and Information Technology, University of Nottingham, Nottingham, England, Feb. 2004.
[5]
S. Gustafson and L. Vanneschi. Operator-based distance for genetic programming: Subtree crossover distance. In Keijzer, M., et al., editor, Genetic Programming, 8th European Conference, EuroGP2005, Lecture Notes in Computer Science, LNCS 3447, pages 178--189. Springer, Berlin, Heidelberg, New York, 2005.
[6]
S. Gustafson and L. Vanneschi. Operator-based tree distance in genetic programming. IEEE Transactions on Evolutionary Computation, 12:4, 2008.
[7]
J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan, 1975.
[8]
P. Kurka. Topological and Symbolic Dynamics. Société Mathmatique de France, Paris, 2003.
[9]
L. Vanneschi. Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland, 2004.
[10]
L. Vanneschi, S. Gustafson, and G. Mauri. Using subtree crossover distance to investigate genetic programming dynamics. In Collet, P., et al., editor, Genetic Programming, 9th European Conference, EuroGP2006, Lecture Notes in Computer Science, LNCS 3905, pages 238--249. Springer, Berlin, Heidelberg, New York, 2006.
Index Terms
- Definition of a crossover based distance for genetic algorithms
Recommendations
A parameter-less genetic algorithm with customized crossover and mutation operators
GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computationGenetic algorithm is one of the well-known population based meta-heuristics. The reasonable performance of the algorithm on a wide variety of problems as well as its simplicity made this algorithm a first choice in lots of cases. However, the algorithm ...
Adding Crossover to Extinction-Based Evolutionary Algorithms
ICCEE '09: Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 02Extinction-based Evolutionary Algorithms (EEA) have been recently developed as the solutions for the problem of early convergence in multimodal optimization tasks. The reproduction of EEAs is done only by mutation. Moreover, according to recent studies, ...
How crossover speeds up building block assembly in genetic algorithms
We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. ...
Comments
Information & Contributors
Information
Published In
July 2010
1520 pages
Copyright © 2010 Copyright is held by the author/owner(s).
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 July 2010
Check for updates
Author Tags
Qualifiers
- Poster
Conference
GECCO '10
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 120Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 24 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in