Abstract
The Operator Equalization (OE) family of bloat control methods have achieved promising results in many domains. In particular, the Flat-OE method, that promotes a flat distribution of program sizes, is one of the simplest OE methods and achieves some of the best results. However, Flat-OE, like all OE variants, can be computationally expensive. This work proposes a simplified strategy for bloat control based on Flat-OE. In particular, bloat is studied in the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. NEAT includes a very simple diversity preservation technique based on speciation and fitness sharing, and it is hypothesized that with some minor tuning, speciation in NEAT can promote a flat distribution of program size. Results indicate that this is the case in two benchmark problems, in accordance with results for Flat-OE. In conclusion, NEAT provides a worthwhile strategy that could be extrapolated to other GP systems, for effective and simple bloat control.
This is a preview of subscription content, log in via an institution to check access.
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
O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11(3-4), 339–363 (2010)
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)
Naredo, E., Trujillo, L., Martínez, Y.: Searching for novel classifiers. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 145–156. Springer, Heidelberg (2013)
Naredo, E., Trujillo, L.: Searching for novel clustering programs. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2013, pp. 1093–1100. ACM, New York (2013)
Trujillo, L., Naredo, E., Martínez, Y.: Preliminary study of bloat in genetic programming with behavior-based search. In: Emmerich, M., et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. AISC, vol. 227, pp. 293–305. Springer, Heidelberg (2013)
Silva, S., Costa, E.: Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141–179 (2009)
Dignum, S., Poli, R.: Operator equalisation and bloat free gp. 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. 110–121. Springer, Heidelberg (2008)
Silva, S., Dignum, S., Vanneschi, L.: Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genetic Programming and Evolvable Machines 13(2), 197–238 (2012)
Silva, S.: Reassembling operator equalisation: a secret revealed. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1395–1402. ACM, New York (2011)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
Langdon, W.B., Poli, R.: Fitness causes bloat. In: Proceedings of the Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer (1997)
Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)
Poli, R., Langdon, W.B., Dignum, S.: On the limiting distribution of program sizes in tree-based genetic programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 193–204. Springer, Heidelberg (2007)
Altenberg, L.: The evolution of evolvability in genetic programming. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 47–74. MIT Press, Cambridge (1994)
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 41–49. Erlbaum Associates Inc., Hillsdale (1987)
Trujillo, L., Olague, G., Lutton, E., Fernández de Vega, F., Dozal, L., Clemente, E.: Speciation in behavioral space for evolutionary robotics. Journal of Intelligent & Robotic Systems 64(3-4), 323–351 (2011)
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
Trujillo, L., Muñoz, L., Naredo, E., Martínez, Y. (2014). NEAT, There’s No Bloat. 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_15
Download citation
DOI: https://doi.org/10.1007/978-3-662-44303-3_15
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)