Lifetime learning as a factor in life history education.
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 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 stimulations for more realistic scenarios are discussed.
Bullinaria
John A
JA
School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK. j.a.bullinaria@cs.bham.ac.uk
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D001185:Artificial Intelligence; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007858:Learning; D008875:Middle Aged; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013997:Time Factors