An interaction between evolution and learning called the Baldwin effect has been known for a century, but it is still poorly appreciated. This paper reports on a computational approach focusing on the quantitative evolution of phenotypic plasticity in complex environment so as to investigate its benefit and cost. For this purpose, we investigate the evolution of connection weights in a neural network under the assumption of epistatic interactions. Phenotypic plasticity is introduced into our model, in which whether each connection weight is plastic or not is genetically defined and connection weights with plasticity can be adjusted by learning. The simulation results have clearly shown that the evolutionary scenario consists of three steps characterized by transitions of the phenotypic plasticity and phenotypic variation, in contrast with the standard interpretation of the Baldwin effect that consists of two steps. We also conceptualize this evolutionary scenario by using a hillclimbing image of a population on a fitness landscape.
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