In this paper, we study the relationship between learning and evolution in a simple abstract model, where neural networks capable of learning are evolved through genetic algorithms (GAs). 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 is a motive force of Lamarckian evolution, we observe adaptational processes of the populations over successive generations. Paying particular attention to behaviours under changing environments, we show the following results. The population with the lower rate of heritability not only shows more stable behaviour against environmental changes, but also maintains 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 toward dynamic environments.