Abstract
Genetic Programming requires that all functions/terminals (tree labels) be given a priori. In the absence of specific information about the solution, the user is often forced to provide a large set, thus enlarging the search space — often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming methods give users the power to specify some rules for valid tree construction, and thus to prune the otherwise unconstrained representation in which Genetic Programming operates. However, in general, the user may not be aware of the best representation space to solve a particular problem. Moreover, some information may be in the form of weak heuristics. In this work, we present a methodology, which automatically adapts the representation for solving a particular problem, by extracting and utilizing such heuristics. Even though many specific techniques can be implemented in the methodology, in this paper we utilize information on local first-order (parent-child) distributions of the functions and terminals. The heuristics are extracted from the population by observing their distribution in “better” individuals. The methodology is illustrated and validated using a number of experiments with the 11-multiplexer. Moreover, some preliminary empirical results linking population size and the sampling rate are also given.
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
Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E., and Francone, Frank D. (1998). Genetic Programming — An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann.
Janikow, Cezary Z. (1996). A methodology for processing problem constraints in genetic programming. Computers and Mathematics with Applications, 32(8):97–113.
Janikow, Cezary Z. and Deshpande, Rahul A (2003). Adaptation of representation in genetic programming. In Dagli, Cihan H., Buczak, Anna L., Ghosh, Joydeep, Embrechts, Mark J., and Ersoy, Okan, editors, Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems, and Artificial Life (ANNIE’2003), pages 45–50. ASME Press.
Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts.
Montana, David J. (1995). Strongly typed genetic programming. Evolutionary Computation, 3(2): 199–230.
Pelikan, Martin and Goldberg, David (1999). Boa: the bayesian optimization algorithm. In Banzhaf, Wolfgang, Daida, Jason, Eiben, Agoston E., Garzon, Max H., Honavar, Vasant, Jakiela, Mark, and Smith, Robert E., editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 525–532, Orlando, Florida, USA. Morgan Kaufmann.
Shan, Y., McKay, R., Abbass, H., and Essam, D. (2003). Program evolution with explicit learning: a new framekwork for program automatic synthesis. Technical report, School of Computer Science, University of New Wales.
Whigham, P. A. (1995). Grammatically-based genetic programming. In Rosca, Justinian P., editor, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 33–41, Tahoe City, California, USA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Janikow, C.Z. (2005). ACGP: Adaptable Constrained Genetic Programming. In: O’Reilly, UM., Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice II. Genetic Programming, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-23254-0_12
Download citation
DOI: https://doi.org/10.1007/0-387-23254-0_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23253-9
Online ISBN: 978-0-387-23254-6
eBook Packages: Computer ScienceComputer Science (R0)