Abstract
The choice of a suitable coding for the application of a GA to optimization problems is often critical to the effectiveness of the GA in finding good solutions. The phenomenon of degeneracy has been pointed out by Radcliffe as one that may be detrimental to GA performance [1, 2]. This problem is characteristic of applications of GAs to such cases as the travelling salesman problem (TSP), neural network design and training, and system identification in control. Previous experimental work by Hancock [3] found that the problem in practice appears less detrimental than expected. In this paper we examine a simple probability model for the occurrence of degenerate crossover that explains why degeneracy causes difficulties in some cases and not others. Experimental results in the case of system identification verify these expectations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
N.J. Radcliffe (1991) Equivalence class analysis of genetic algorithms. Complex Systems, 5, 183–205.
N.J. Radcliffe (1993) Genetic set recombination and its application to neural network topology optimisation. Neural Computing and Applications, 1, 67–90.
P.J.B. Hancock (1992) Genetic algorithms and permutation problems: A comparison of recombination operators for neural net structure specification. In L.D. Whitley and J.D. Schaffer (Eds.) (1992) Proceedings of • COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, L.D.Whitley and J.D.Schaffer (eds.), IEEE Computer Society Press, Los Alamitos, CA, 108–122.
J.D. Schaffer, D. Whitley and L.J. Eshelman (1992) Combinations of genetic algorithms and neural networks: A survey of the state of the art. In L.D. Whitley and J.D. Schaffer (Eds.) (1992) Proceedings of COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, IEEE Computer Society Press, Los Alamitos, CA, 1–37.
N.J. Radcliffe and P. Surry (1995) Formae and the variance of fitness. In D. Whitley and M. Vose (1995) Foundations of Genetic Algorithms 3, Morgan Kaufmann, San Mateo, CA, 51–72.
R. Nambiar and P. Mars (1992) Genetic algorithms for adaptive digital filtering. Proc. IEE Colloquium on Genetic Algorithms for Control and Systems Engineering. Digest No.1992/106, IEE, London.
Ping Dai (1997) Hybrid Genetic Algorithms with Application to System Identification and Control. MPhil dissertation, Control Theory and Applications Centre, Coventry University, UK.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Wien
About this paper
Cite this paper
Reeves, C.R., Dai, P. (1999). The Effect of Degenerate Coding on Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6384-9_36
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
eBook Packages: Springer Book Archive