Abstract
Domain knowledge is essential for successful problem solving and optimization. This paper introduces a framework in which a form of automatic domain knowledge extraction can be implemented using concepts from the field of machine learning. The result is an encoding of the type used in most evolutionary computation (EC) algorithms. The approach focuses on whole problem domains instead of single problems. After the theoretical validation of the algorithm the main idea is given impetus by showing that on different subdomains of linear functions the method finds different encodings which result in different problem complexities.
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
Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, 1999. Morgan Kaufmann.
Márk Jelasity. A wave analysis of the subset sum problem. In Thomas Back, editor, Proceedings of the Seventh International Conference on Genetic Algorithms, pages 89–96, San Francisco, California, 1997. Morgan Kaufmann.
Márk Jelasity. The adaptationist stance and evolutionary computation. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, 1999. Morgan Kaufmann Banzhaf et al. [1], pages 1859–1864.
Mark Jelasity and Jozsef Dombi. Implicit formae in genetic algorithms. In W. Ebeling, Ingo Rechenberg, Hans-Paul Schwefel, and Hans-Michael Voigt, editors, Parallel Problem Solving from Nature-PPSN IV, volume 1141 of Lecture Notes in Computational Science, pages 154–163. Springer-Verlag, 1996.
M. Mitchell, J. H. Holland, and S. Forrest. When will a genetic algorithm outperform hill-climbing? In J. D. Cowan et al., editors, Advances in Neural Information Processing Systems 6. Morgan Kaufmann, 1994.
Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
Masaharu Munetomo and David E. Goldberg. Identifying linkage groups by nonlinearity/non-monotonicity detection. In Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors. GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, 1999. Morgan Kaufmann Banzhaf et al. [1], pages 433–440.
Martin Pelikan, David E. Goldberg, and Fernando Lobo. A survey of optimization by building and using probablistic models. Technical Report 99018, Illinois Genetic Algorithms Laboratory, 1999.
J. Ross Quinlan. Induction of decision trees. Machine Learning, l(l):81–106, 1986.
Nicholas J. Radcliffe. The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence, 10(4):339–384, 1994.
Alexander I. Rudnicky, Alexander G. Hauptmann, and Kai-fu Lee. Survey of current speech technology. Communications of the ACM, 37(3):52–57, March 1994.
David H. Wolpert and William G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, l(l):67–82, April 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jelasity, M. (2000). Towards Automatic Domain Knowledge Extraction for Evolutionary Heuristics. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_74
Download citation
DOI: https://doi.org/10.1007/3-540-45356-3_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41056-0
Online ISBN: 978-3-540-45356-7
eBook Packages: Springer Book Archive