Abstract
Synthesizing a program with the desired input-output behavior by means of genetic programming is an iterative process that needs appropriate guidance. That guidance is conventionally provided by a fitness function that measures the conformance of program output with the desired output. Contrary to widely adopted stance, there is no evidence that this quality measure is the best choice; alternative search drivers may exist that make search more effective. This study proposes and investigates a new family of behavioral search drivers, which inspect not only final program output, but also program behavior meant as the partial results it arrives at while executed.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Krawiec, K., Swan, J.: Pattern-guided genetic programming. In: Blem, C., et al. (eds.) GECCO 2013: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, Amsterdam, The Netherlands, pp. 949–956. ACM (2013)
Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann (1992)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Quinlan, J.R., Rivest, R.L.: Inferring decision trees using the minimum description length principle. Inf. Comput. 80(3), 227–248 (1989)
Smith, R., Forrest, S., Perelson, A.: Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2) (1993)
Krawiec, K., Lichocki, P.: Using co-solvability to model and exploit synergetic effects in evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 492–501. Springer, Heidelberg (2010)
Tomassini, M., Vanneschi, L., Collard, P., Clergue, M.: A study of fitness distance correlation as a difficulty measure in genetic programming. Evolutionary Computation 13(2), 213–239 (2005)
Iba, H., Sato, T., de Garis, H.: System identification approach to genetic programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, vol. 1, pp. 401–406. IEEE Press (1994)
Zhang, B.T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)
McPhee, N.F., Ohs, B., Hutchison, T.: Semantic building blocks in genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 134–145. Springer, Heidelberg (2008)
Krawiec, K., Bhanu, B.: Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation 11(5), 635–650 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krawiec, K., O’Reilly, UM. (2014). Behavioral Search Drivers for Genetic Programing. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-662-44303-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44302-6
Online ISBN: 978-3-662-44303-3
eBook Packages: Computer ScienceComputer Science (R0)