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.
- Published in
- Lecture Notes in Computer Science. 17th European Conference, EuroGP 2014.
- Date of conference
- April 23 - 25, 2014
Latest from Leonardo Trujillo
- EEG classification for the detection of mental states
- Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer
- Prediction of energy performance of residential buildings: a genetic programming approach
- Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm
- Seizure States Identification in Experimental Epilepsy using Gabor Atom Analysis