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NEAT, There’s No Bloat

  • Leonardo Trujillo
  • Luis Muñoz
  • Enrique Naredo
  • Yuliana Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)

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.

Keywords

NEAT Bloat Operator Equalization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Leonardo Trujillo
    • 1
  • Luis Muñoz
    • 1
  • Enrique Naredo
    • 1
  • Yuliana Martínez
    • 1
  1. 1.Tree-Lab, Doctorado en Ciencias de la Ingeniería, Departamento de Ingeniería, Eléctrica y ElectrónicaInstituto Tecnológico de TijuanaTijuana B.C.México

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