Abstract
This paper studies Genetic Programming (GP) and its relation to the Genetic Algorithm (GA). GP uses a GA approach to breed successive populations of programs, represented in the chromosomes as parse trees, until a program that solves the problem emerges. However, parse trees are not naturally homologous, consequently changes had to be introduced into GP. To better understand these changes it would be instructive if a canonical GA could also be used to perform program induction. To this end an appropriate GA representation scheme is developed (called EP-I for Evolutionary Programming with Introns). EP-I has been tested on three problems and performed identically to GP, thus demonstrating that the changes introduced by GP do not have any properties beyond those of a canonical GA for program induction. EP-I is also able to simulate GP exactly thus gaining further insights into the nature of GP as a GA.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Banzhaf, W.,“Genetic Programming for Pedestrians”. In Proceedings of the Fiffh International Conference on Genetic Algorithms, 1993, edited by S. Forrest. San Mateo, California: Morgan Kaufmann Publishers Inc., pg. 628.
Cramer, N.L., “A Representation for the Adaptive Generation of Simple Sequential Programs”. In Proceedings of an International Conference on Genetic Algorithms and their Applications, 1985, edited by J.J. Grefenstette. Hillsdale, NJ: Lawrence Erlbaum Associates, 183–187.
Crick, F., “Split Genes and RNA Splicing” Science, Vol. 204 (April 20, 1979): 264–271.
De Jong, K. A., “On Using Genetic Algorithms to Search Program Spaces”. In Proceedings of the 2nd International Conference on Genetic Algorithms, 1987, editor J.J. Grefenstette. Hillsdale, NJ: Lawrence Erlbaum Associates 210–216.
Fujiki, C., and J. Dickinson, “Using the Genetic Algorithm to Generate LISP Source Code to Solve the Prisoner's Dilemma”. In Proceedings of the 2nd International Conference on Genetic Algorithms, 1987, editor J.J. Grefenstette. Hillsdale, NJ: Lawrence Erlbaum Associates, 236–240.
Goldberg, David E., Genetic Algorithms in Search, Optimization & Machine Learning. Reading, Massachusetts: Addison-Wesley Publishing Company, Inc., 1989.
Holland, John H., Adaptation in Natural and Artificial Systems. Cambridge, Massachusetts: MIT Press, 1992; first published University of Michigan, 1975.
Koza, John R., Genetic Programming: On the Programming of Computers by means of Natural Selection. Cambridge Massachusetts: MIT Press, 1992.
O'Reilly, U.-M., and F. Oppacher, “The Troubling Aspects of a Building Block Hypothesis for Genetic Programming”. To appear in Foundations of Genetic Algorithms 3, 1994.
Wineberg, M, and F. Oppacher, “A Canonical Genetic Algorithm Based Approach to Genetic Programming” to appear in the proceedings of the ECAI-94 Workshop on Applied Genetic and Other Evolutionary Algorithms, 1994.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wineberg, M., Oppacher, F. (1994). A representation scheme to perform program induction in a canonical genetic algorithm. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_273
Download citation
DOI: https://doi.org/10.1007/3-540-58484-6_273
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58484-1
Online ISBN: 978-3-540-49001-2
eBook Packages: Springer Book Archive