Abstract
Genetic algorithms with tree structures to act as genes have some special properties which are different from that of string genetic algorithms. In this paper, a definition of schema for tree structures is proposed and a credit value is assigned to every node and every arc of a tree; a new concept, frame arc, is introduced to describe the structure of a schema. A credit partitioning mechanism, Nourishment Mechanism, which exploits a tree's two dimensional property is presented. We show that a schema with higher total credit value has a larger probability to survive in a genetic algorithm with nourishment mechanism than in an original genetic algorithm without nourishment mechanism. Accumulation of node credits is also calculated.
Partially suported by 863 High Tech. Project. NSF Project. National-95' Key Project. MADIS Lab.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Bickel, A. S. and Bickel R. W., 1987, Tree Structured Rules in Genetic Algorithms, In Davis, Lawrence (editor), Genetic Algorithms and Simulated Annealing, Pittman.
D'haeseleer Patrik, 1994, Context Preserving Crossover in Genetic Programming, Proceedings of The First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Vol. 1.256–261.
De Jong, K. A., 1994, Genetic Algorithms: A 25 Year Perspective, Computational Intelligence: Imitating Life, IEEE Neural Networks Council.
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley Publishing Company, Inc.
Fujiki, Cory and Dickinson, John, 1987, Using the Genetic Algorithm to Generate LISP Source Code to Solve the Prisoner's Dilemma, In Grefenstette, John J. (editor), Genetic Algorithms and Their Applications, Proceedings of the Second International Conference On Genetic Algorithms, Erlbaum.
Holland, J. H., 1975, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press.
Holland, J. H., 1986, Escaping brittleness: The Possibilities of General-purpose Learning Algorithms Applied to Parallel Rule-based Systems, In Michalski, Ryszard S., et al. (editors), Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann.
Koza, J. R., 1992, Genetic Programming: On the programming of Computers by Means of Natural Selection. The MIT Press.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, Z., Lu, R. (1997). Tree structure genetic algorithm with a nourishment mechanism. In: Jiang, T., Lee, D.T. (eds) Computing and Combinatorics. COCOON 1997. Lecture Notes in Computer Science, vol 1276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0045115
Download citation
DOI: https://doi.org/10.1007/BFb0045115
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63357-0
Online ISBN: 978-3-540-69522-6
eBook Packages: Springer Book Archive