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The effect of learning on life history evolution

Published: 07 July 2007 Publication History

Abstract

A series of evolutionary neural network simulations are presented which explore the hypothesis that learning factors can result in the evolution of long periods of parental protection and late onset of maturity. By evolving populations of neural networks to learn quickly to perform well on simple classification tasks, it is shown that better learned performance is obtained if protection from competition is provided during the network's early learning period. Moreover, if the length of the protection period is allowed to evolve, it does result in the emergence of relatively long protection periods, even if there are other costs involved, such as individuals not being allowed to reproduce during their protection phase, and the parents suffering increased risk of dying while protecting their offspring.

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  • (2009)Modeling, simulating, and simplifying links between stress, attachment, and reproductionBehavioral and Brain Sciences10.1017/S0140525X0900021132:1(39-40)Online publication date: 12-Feb-2009

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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Published: 07 July 2007

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  1. artificial life
  2. evolution
  3. learning
  4. life histories

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  • (2009)Modeling, simulating, and simplifying links between stress, attachment, and reproductionBehavioral and Brain Sciences10.1017/S0140525X0900021132:1(39-40)Online publication date: 12-Feb-2009

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