Learning and Evolution in Neural Networks

Abstract

This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learning task (predicting the next position of food on the basis of present position and planned network's movement) are different tasks. In these conditions, learning influences evolution (without Lamarckian inheritance of learned weight changes) and evolution influences learning. Average but not peak fitness has a better evolutionary growth with learning than without learning. After the initial generations, individuals that learn to predict during life also improve their food-finding ability during life. Furthermore, individuals that inherit an innate capacity to find food also inherit an innate predisposition to learn to predict the sensory consequences of their movements. They do not predict better at birth, but they do learn to predict better than individuals of the initial generation given the same learning experience. The results are interpreted in terms of a notion of dynamic correlation between the fitness surface and the learning surface. Evolution succeeds in finding both individuals that have high fitness and individuals that, although they do not have high fitness at birth, end up with high fitness because they learn to predict.