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      A model for the interaction of learning and evolution.

      Bulletin of Mathematical Biology
      Algorithms, Alleles, Animals, Biological Evolution, Genotype, Humans, Learning, Neural Networks (Computer), Numerical Analysis, Computer-Assisted, Phenotype, Synapses, genetics

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          Abstract

          We present a simple model in order to discuss the interaction of the genetic and behavioral systems throughout evolution. This considers a set of adaptive perceptrons in which some of their synapses can be updated through a learning process. This framework provides an extension of the well-known Hinton and Nowlan model by blending together some learning capability and other (rigid) genetic effects that contribute to the fitness. We find a halting effect in the evolutionary dynamics, in which the transcription of environmental data into genetic information is hindered by learning, instead of stimulated as is usually understood by the so-called Baldwin effect. The present results are discussed and compared with those reported in the literature. An interpretation is provided of the halting effect.

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