Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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 effect of learning on life history evolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using evolution to improve neural network learning: pitfalls and solutions
Neural Computing and Applications
The altricial-precocial spectrum for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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An artificial life approach is taken to explore the effect that lifetime learning can have on the evolution of certain life history traits, in particular the periods of protection that parents offer their young, and the age at first reproduction of those young. The study begins by simulating the evolution of simple artificial neural network systems that must learn quickly to perform well on simple classification tasks, and determining if and when extended periods of parental protection emerge. It is concluded that longer periods of parental protection of children do offer clear learning advantages and better adult performance, but only if procreation is not allowed during the protection period. In this case, a compromise protection period evolves that balances the improved learning performance against reduced procreation period. The crucial properties of the neural learning processes are then abstracted out to explore the possibility of studying the effect of learning more generally and with better computational efficiency. Throughout, the implications of these simulations for more realistic scenarios are discussed.