To : All Subj: Interesting paper Parisi, Domenico, Stefano Nolfi, and Federico Cecconi. Le

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From: Wesley R. Elsberry 12 Jul 94 08:59 To : All Subj: Interesting paper Parisi, Domenico, Stefano Nolfi, and Federico Cecconi. Learning, behavior, and evolution. Technical Report PCIA-91-14, Institute of Psychology (C.N.R. - Rome). [Quote] 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 (1) when learning tasks correlate with the fitness criterion, and (2) 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. [End quote] ===================== Parisi et al. note that learning and behavior tend to get short shrift when it comes to evolutionary theories. They cite the index to Mayr's "Growth of biological thought", where only three entries exist for "behavior", and no entries for "learning" can be found. Parisi et al. use neural networks and genetic algorithms to explore some of these neglected issues. In a population of neural networks whose inputs are angle and distance to "food" sources and whose outputs control "motor actions", normal GA techniques result in an increase in performance of food finding over time. Then, a learning task is added: the neural networks now also predict the outcome of motor output, that is, a prediction is made of what the sensory input at the next time step will be. No learned change is transmitted to offspring: the untrained NN representation is what is acted upon by the GA. However, with the learning task in place, much better food finding performance emerges faster in the population. A supplementary learning task which is not evaluated in determining fitness of the phenotype was added. For this, Parisi et al. chose the exclusive-or problem. The naive expectation was that no correlated increase in XOR learning would be observed, since XOR learning performance was not evaluated. However, further generations of the NNs did learn the XOR task better and faster. All in all, this was a very intriguing paper.

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