The Effect of Learning on Life History Evolution

John Bullinaria

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationGECCO '07 Proceedings of the 9th annual conference on Genetic and evolutionary computation
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)978-1-59593-697-4
Publication statusPublished - 7 Jul 2007
Event9th Genetic and Evolutionary Computation Conference (GECCO 2007) - London, United Kingdom
Duration: 7 Jul 200711 Jul 2007


Conference9th Genetic and Evolutionary Computation Conference (GECCO 2007)
Country/TerritoryUnited Kingdom


  • Learning
  • Life Histories
  • Evolution
  • Artificial Life


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