NEAT, There’s No Bloat
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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.
KeywordsNEAT Bloat Operator Equalization
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- 5.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)Google Scholar
- 9.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)Google Scholar
- 11.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)Google Scholar
- 14.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)Google Scholar
- 15.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)Google Scholar