The effect of learning on life history evolution

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. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence – intelligent agents, multiagent systems. General Terms Algorithms, Measurement...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: John A. Bullinaria
• Keywords: GECCO 2007, Neural Network Simulations, Neural Networks, Protection Periods