Abstract
The problem of setting the mutation step-size for real-coded evolutionary algorithms has received different answers: exogenous rules like the 1/5 rule, or endogenous factor like the self-adaptation of the step-size in the Gaussian mutation of modern Evolution Strategies. On the other hand, in the bitstring framework, the control of both crossover and mutation by means of Inductive Leaning has proven beneficial to evolution, mostly by recognizing — and forbidding — past errors (i.e. crossover or mutations leading to offspring that will not survive next selection step). This Inductive Learning-based control is transposed to the control of mutation step-size in evolutionary parameter optimization, and the resulting algorithm is experimentally compared to the self-adaptive step-size of Evolution Strategies.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
P.J. Angeline. The effects of noise on self-adaptive evolutionary optimization. In L. J. Fogel, P. J. Angeline, and T. Bäck, editors, Proceedings of the 5 th Annual Conference on Evolutionary Programming, pages 433–439. MIT Press, 1996.
T. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1–23, 1993.
R. A. Caruna and J. D. Schaffer. Representation and hidden bias: Gray vs binary coding for genetic algorithms. In Proceedings of ICML-88, International Conference on Machine Learning. Morgan Kaufmann, 1988.
L. Davis. Adapting operator probabilities in genetic algorithms. In J. D. Schaffer, editor, Proceedings of the 3 rd International Conference on Genetic Algorithms, pages 61–69. Morgan Kaufmann, 1989.
L. Eshelman and J. D. Schaffer. Real-coded genetic algorithms and interval-schemata. In L. D. Whitley, editor, Foundations of Genetic Algorithms 2, pages 187–202, Los Altos, CA, 1993. Morgan Kaufmann.
D. B. Fogel. An analysis of evolutionary programming. In D. B. Fogel and W. Atmar, editors, Proceedings of the 1 st Annual Conference on Evolutionary Programming, pages 43–51. Evolutionary Programming Society, 1992.
D. B. Fogel, L. J. Fogel, W. Atmar, and G. B. Fogel. Hierarchic methods of evolutionary programming. In D. B. Fogel and W. Atmar, editors, Proceedings of the 1 st Annual Conference on Evolutionary Programming, pages 175–182, La Jolla, CA, 1992. Evolutionary Programming Society.
D. B. Fogel and A. Ghozeil. Using fitness distributions to design more efficient evolutionary computations. In T. Fukuda, editor, Proceedings of the Third IEEE International Conference on Evolutionary Computation, pages 11–19. IEEE, 1996.
L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. New York: John Wiley, 1966.
D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.
J.H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975.
C. Z. Janikow and Z. Michalewicz. An experimental comparison of binary and floating point representations in genetic algorithms. In R. K. Belew and L. B. Booker, editors, Proceedings of 4th International Conference on Genetic Algorithms, pages 31–36. Morgan Kaufmann, July 1991.
T. Jones. Crossover, macromutation and population-based search. In L. J. Eshelman, editor, Proceedings of the 6 th International Conference on Genetic Algorithms, pages 73–80. Morgan Kaufmann, 1995.
T. Jones and S. Forrest. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In L. J. Eshelman, editor, Proceedings of the 6 th International Conference on Genetic Algorithms, pages 184–192. Morgan Kaufmann, 1995.
Y. Kodratoff. Introduction to Machine Learning. Pitman Publishing, London, 1988.
Z. Michalewicz. Genetic Algorithms+Data Structures=Evolution Programs. Springer Verlag, New-York, 1996. 3rd edition.
R.S. Michalski. A theory and methodology of inductive learning. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: an artificial intelligence approach, volume 1, pages 83–134. Morgan Kaufmann, 1983.
T.M. Mitchell. Generalization as search. Artificial Intelligence, 18:203–226, 1982.
J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.
N. J. Radcliffe. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183–20, 1991.
N. J. Radcliffe. Forma analysis and random respectful recombination. In R. K. Belew and L. B. Booker, editors, Proceedings of the 4 th International Conference on Genetic Algorithms, pages 222–229. Morgan Kaufmann, 1991.
C. Ravisé and M. Sebag. An advanced evolution should not repeat its past errors. In L. Saitta, editor, Proceedings of the 13 th International Conference on Machine Learning, pages 400–408, 1996.
C. Ravisé, M. Sebag, and M. Schoenauer. An induction-based control for genetic algorithms. In J.-M. Alliot, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers, editors, Artificial Evolution. Springer-Verlag, 1996.
I. Rechenberg. Evolutionstrategie: Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution. Fromman-Holzboog Verlag, Stuttgart, 1973.
G. Rudolph. Convergence of non-elitist strategies. In Z. Michalewicz, J. D. Schaffer, H.-P. Schwefel, D. B. Fogel, and H. Kitano, editors, Proceedings of the First IEEE International Conference on Evolutionary Computation, pages 63–66. IEEE Press, 1994.
N. Saravanan, D. B. Fogel, and K. M. Nelson. A comparison of methods for self-adaptation in evolutionary algorithms. Biosystems, 36:157–166, 1995.
H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley & Sons, New-York, 1981. 1995 — 2nd edition.
M. Sebag. Delaying the choice of bias: A disjunctive version space approach. In L. Saitta, editor, Proceedings of the 13 th International Conference on Machine Learning, pages 444–452. Morgan Kaufmann, 1996.
M. Sebag and M. Schoenauer. Controlling crossover through inductive learning. In Y. Davidor, H.-P. Schwefel, and R. Manner, editors, Proceedings of the 3 rd Conference on Parallel Problems Solving from Nature. Springer-Verlag, LNCS 866, 1994.
A. Törn and A. Zilinskas. Global Optimization. Springer Verlag, New-York, 1989.
A. Wright. Genetic algorithms for real parameter optimization. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, pages 205–218. Morgan Kaufmann, 1991.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sebag, M., Schoenauer, M., Ravisé, C. (1997). Inductive learning of mutation step-size in evolutionary parameter optimization. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014816
Download citation
DOI: https://doi.org/10.1007/BFb0014816
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62788-3
Online ISBN: 978-3-540-68518-0
eBook Packages: Springer Book Archive