Operator equalisation for bloat free genetic programming and a survey of bloat control methods

  • Authors:

  • Sara Silva;Stephen Dignum;Leonardo Vanneschi

  • Affiliations:

  • INESC-ID Lisboa, Lisboa, Portugal 1000-029 and Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal;School of Computer Science and Electronic Engineering, University of Essex, Essex, UK;INESC-ID Lisboa, Lisboa, Portugal 1000-029 and ISEGI, Universidade Nova de Lisboa, Lisboa, Portugal 1070-312 and D.I.S.Co., University of Milano-Bicocca, Milan, Italy

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2012

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Abstract

Bloat can be defined as an excess of code growth without a corresponding improvement in fitness. This problem has been one of the most intensively studied subjects since the beginnings of Genetic Programming. This paper begins by briefly reviewing the theories explaining bloat, and presenting a comprehensive survey and taxonomy of many of the bloat control methods published in the literature through the years. Particular attention is then given to the new Crossover Bias theory and the bloat control method it inspired, Operator Equalisation (OpEq). Two implementations of OpEq are described in detail. The results presented clearly show that Genetic Programming using OpEq is essentially bloat free. We discuss the advantages and shortcomings of each different implementation, and the unexpected effect of OpEq on overfitting. We observe the evolutionary dynamics of OpEq and address its potential to be extended and integrated into different elements of the evolutionary process.