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The Baldwin Effect Revisited: Three Steps Characterized by the Quantitative Evolution of Phenotypic Plasticity

  • Reiji Suzuki
  • Takaya Arita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

Abstract

An interaction between evolution and learning called the Baldwin effect has been known for a century, but it is still poorly appreciated. This paper reports on a computational approach focusing on the quantitative evolution of phenotypic plasticity in complex environment so as to investigate its benefit and cost. For this purpose, we investigate the evolution of connection weights in a neural network under the assumption of epistatic interactions. Phenotypic plasticity is introduced into our model, in which whether each connection weight is plastic or not is genetically defined and connection weights with plasticity can be adjusted by learning. The simulation results have clearly shown that the evolutionary scenario consists of three steps characterized by transitions of the phenotypic plasticity and phenotypic variation, in contrast with the standard interpretation of the Baldwin effect that consists of two steps. We also conceptualize this evolutionary scenario by using a hill-climbing image of a population on a fitness landscape.

Keywords

Phenotypic Variation Phenotypic Plasticity Epistatic Interaction Connection Weight Evolutionary Scenario 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Reiji Suzuki
    • 1
  • Takaya Arita
    • 1
  1. 1.Graduate School of Human InformaticsNagoya UniversityNagoyaJapan

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