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HybrID: A Hybridization of Indirect and Direct Encodings for Evolutionary Computation

  • Jeff Clune
  • Benjamin E. Beckmann
  • Robert T. Pennock
  • Charles Ofria
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5778)

Abstract

Evolutionary algorithms typically use direct encodings, where each element of the phenotype is specified independently in the genotype. Because direct encodings have difficulty evolving modular and symmetric phenotypes, some researchers use indirect encodings, wherein one genomic element can influence multiple parts of a phenotype. We have previously shown that HyperNEAT, an indirect encoding, outperforms FT-NEAT, a direct-encoding control, on many problems, especially as the regularity of the problem increases. However, HyperNEAT is no panacea; it had difficulty accounting for irregularities in problems. In this paper, we propose a new algorithm, a Hybridized Indirect and Direct encoding (HybrID), which discovers the regularity of a problem with an indirect encoding and accounts for irregularities via a direct encoding. In three different problem domains, HybrID outperforms HyperNEAT in most situations, with performance improvements as large as 40%. Our work suggests that hybridizing indirect and direct encodings can be an effective way to improve the performance of evolutionary algorithms.

Keywords

Indirect (generative developmental) encodings (representations) artificial neural networks neuroevolution evolutionary algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jeff Clune
    • 1
  • Benjamin E. Beckmann
    • 1
  • Robert T. Pennock
    • 1
    • 2
  • Charles Ofria
    • 1
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Department of Philosophy and Lyman Briggs CollegeMichigan State UniversityEast LansingUSA

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