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.
Original language | English |
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Title of host publication | GECCO '07 Proceedings of the 9th annual conference on Genetic and evolutionary computation |
Publisher | Association for Computing Machinery (ACM) |
Pages | 222-229 |
Number of pages | 8 |
ISBN (Print) | 978-1-59593-697-4 |
DOIs | |
Publication status | Published - 7 Jul 2007 |
Event | 9th Genetic and Evolutionary Computation Conference (GECCO 2007) - London, United Kingdom Duration: 7 Jul 2007 → 11 Jul 2007 |
Conference
Conference | 9th Genetic and Evolutionary Computation Conference (GECCO 2007) |
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Country/Territory | United Kingdom |
City | London |
Period | 7/07/07 → 11/07/07 |
Keywords
- Learning
- Life Histories
- Evolution
- Artificial Life