Abstract
This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery problems which have been previously tackled only with GP. All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm [8]. We feel it is not intuitively obvious that mutation-based adaptive search can handle program discovery yet, to date, for each GP problem we have tried, SA or SIHC also work.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. Wiley. 1989
Cramer, N. L.: A Representation for the Adaptive Generation of Simple Sequential Programs. Proc of Ist Intl Conf on Genetic Algorithms. Lawrence Erlbaum Assoc. 1985.
Friedberg, R.M.: A Learning Machine: Part 1. IBM Journal of Research and Development. 2(1): 2–13 (1958)
Friedberg, R.M., Dunham B., North J.H.: A Learning Machine: Part 2. IBM Journal of Research and Development. 3(3): 282–287 (1959)
Fujicki, C., Dickinson J.: Using the Genetic Algorithm to Generate LISP Source Code to Solve the Iterated Prisoner's Dilemma. Proc. of the 2nd Intl Conf on Genetic Algorithms. Lawrence Erlbaum Assoc. 1987.
Goldberg, D. E.: Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley, 1989.
Holland, J. H.: Adaptation in Natural and Artificial Systems: An Introductory analysis with applications to biology, control, and artificial intelligence. 2nd Ed MIT Press,1992. (1st Ed 1978)
Koza, J. R.: Genetic Programming; On the Programming of Computers by Means of Natural Selection. Bradford Books, 1992.
Koza, J. R.: Genetic Programming II. Bradford Books, 1994.
Lenat, D. B: The Role of Heuristics in Learning by Discovery: Three Case Studies. Machine Learning, Eds. R. S. Michalski, J. G. Carbonell and T. M. Mitchell. Tioga Publishing Inc, 1983.
Sankoff, S., Kruskal, J.B., Editors: Time Warps, String Edits and Macromolecules: the Theory and Practice of Sequence Comparison. Addison Wesley, 1983.
Smith, S., J.: Flexible Learning of Problem Solving Heuristics Through Adaptive Search. Proc of the 8th Intl Joint Conf on Artificial Intelligence. Morgan Kaufmann, 1983.
Press, W. H.: Numerical Recipes in C: the art of scientific computing. Cambridge University Press, 1992.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
O'Reilly, UM., Oppacher, F. (1994). Program search with a hierarchical variable length representation: Genetic Programming, simulated annealing and hill climbing. 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_283
Download citation
DOI: https://doi.org/10.1007/3-540-58484-6_283
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