Abstract
In this paper, we provided an extension of our previous work on adaptive genetic algorithm [1]. Each individual encodes the probability (rate) of its genetic operators. In every generation, each individual is modified by only one operator. This operator is selected according to its encoded rates. The rates are updated according to the performance achieved by the offspring (compared to its parents) and a random learning rate. The proposed approach is augmented with a simple transposition operator and tested on a number of benchmark functions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
J. Gomez and D. Dasgupta, “Using competitive operators and a local selection scheme in genetic search,” in Late-breaking papers GECCO 2002, 2002.
A. Tuson and P. Ross, “Adapting operator settings in genetic algorithms,” Evolutionary Computation, 1998.
F. Lobo, The parameter-less genetic algorithm: rational and automated parameter selection for simplified genetic algorithm operation. PhD thesis, Nova University of Lisboa, 2000.
L. Davis, “Adapting operator probabilities in genetic algorithms,” in Third International Conference on Genetic Algorithms and their Applications, pp. 61–69, 1989.
B. Julstrom, “What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm,” in Sixth International Conference on Genetic Algorithms, pp. 81–87, 1995.
J. Digalakis and K. Margaritis, “An experimental study of benchmarking functions for genetic algorithms,” in IEEE Conferences Transactions, Systems, Man and Cybernetics, vol. 5, pp. 3810–3815, 2000.
A. Patton, T. Dexter, E. Goodman, and W. Punch, “On the application of cohort-driven operators to continuous optimization problems using evolutionary computation,” Evolutionary Programming, no. 98, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gómez, J., Dasgupta, D., González, F. (2003). Using Adaptive Operators in Genetic Search. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_34
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive