Comparison between Lamarckian and Darwinian Evolution on a Model Using Neural Networks and Genetic Algorithms

In this paper, we study the relationship between learning and evolution in a simple abstract model, where neural networks capable of learning are evolved using genetic algorithms (GAs). Each individual tries to acquire a proper behavior under a given environment through its lifetime learning, and the best individuals are selected to reproduce offspring, which then conduct lifetime learning in the succeeding generation. The connective weights of individuals' neural networks undergo modification, i.e., certain characters will be acquired, through their lifetime learning. By setting various rates for the heritability of acquired characters, which control the strength of ‘Lamarckian’ strategy, we observe adaptational processes of populations over successive generations. By taking the degree of environmental changes into consideration, we show the following results. Under static environments, populations with higher rates of heritability adapt themselves more quickly toward the environments, and thus perform well. On the other hand, under nonstationary environments, populations with lower rates of heritability not only show more stable behavior against environmental changes, but also maintain greater adaptability with respect to such changing environments. Consequently, the population with zero heritability, i.e., the Darwinian population, attains the highest level of adaptation towards dynamic environments. © 2000, Springer-Verlag London Limited.