Evolving efficient learning algorithms for binary mappings
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Evolved Age Dependent Plasticity Improves Neural Network Performance
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
The altricial-precocial spectrum for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Lifetime learning as a factor in life history evolution
Artificial Life
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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.