Advertisement

Tree structure genetic algorithm with a nourishment mechanism

  • Zhangang Han
  • Ruqian Lu
Session 14: Mathematical Programming and Genetic Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1276)

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.

Keywords

genetic algorithms genetic programming crossover credit partitioning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bickel, A. S. and Bickel R. W., 1987, Tree Structured Rules in Genetic Algorithms, In Davis, Lawrence (editor), Genetic Algorithms and Simulated Annealing, Pittman.Google Scholar
  2. 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.Google Scholar
  3. De Jong, K. A., 1994, Genetic Algorithms: A 25 Year Perspective, Computational Intelligence: Imitating Life, IEEE Neural Networks Council.Google Scholar
  4. Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley Publishing Company, Inc.Google Scholar
  5. 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.Google Scholar
  6. Holland, J. H., 1975, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press.Google Scholar
  7. 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.Google Scholar
  8. Koza, J. R., 1992, Genetic Programming: On the programming of Computers by Means of Natural Selection. The MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Zhangang Han
    • 1
    • 2
  • Ruqian Lu
    • 2
  1. 1.Systems Science DepartmentBeijing Normal UniversityBeijingP. R. China
  2. 2.Institute of Mathematics, Academia SinicaBeijingP. R. China

Personalised recommendations