Technical Report Available
stefano nolfi
STIVA%IRMKANT.BITNET at vma.cc.cmu.edu
Thu Oct 3 11:41:47 EDT 1991
The following technical report is available.
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Learning, Behavior, and Evolution
Domenico Parisi Stefano Nolfi Federico Cecconi
Institute of Psychology
CNR - Rome
e-mail: stiva at irmkant.Bitnet
Abstract
We present simulations of evolutionary processes operating on populations
of neural networks to show how learning and behavior can influence
evolution within a strictly Darwinian framework. Learning can accelerate
the evolutionary process both when learning tasks correlated with the
fitness criterion and when random learning tasks are used. Furthermore,
an ability to learn a task can emerge and be transmitted evolutionarily
for both correlated and uncorrelated tasks. Finally, behavior that allows
the individual to self-select the incoming stimuli can influence evolution
by becoming one of the factors that determine the observed phenotypic
fitness on which selective reproduction is based. For all the effects
demonstrated, we advance a consistent explanation in terms of a
multidimensional weight space for neural networks, a fitness surface for
the evolutionary task, and a performance surface for the learning task.
This paper will be presented at ECAL-91 - European Conference on Artificial
Life, December 1991, Paris.
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