Phenotypic plasticity in evolving neural networks

We present a model based on genetic algorithm and neural networks. The neural networks develop on the basis of an inherited genotype but they show phenotypic plasticity, i.e. they develop in ways that are adapted to the specific environment The genotype-to-phenotype mapping is not abstractly conceived as taking place in a single instant but is a temporal process that takes a substantial portion of an individual's lifetime to complete and is sensitive to the particular environment in which the individual happens to develop. Furthermore, the respective roles of the genotype and of the environment are not decided a priori but are part of what evolves. We show how such a model is able to evolve control systems for autonomous robots that can adapt to different types of environments.