How the “Baldwin Effect” Can Guide Evolution in Dynamic Environments

Abstract

Evolution and learning are two different ways in which the organism can adapt their behaviour to cope with problems posed by the environment. The second type of adaptation occurs when individuals exhibit plasticity in response to environmental conditions that may strengthen their survival. Individuals seek a behaviour that increases fitness. Therefore, it is plausible and rational for the individual to have some learning capabilities to prepare for the uncertain future, some sort of prediction or plastic abilities in different environments. Learning has been shown to benefit the evolutionary process through the Baldwin Effect, enhancing the adaptivity of the evolving population. In nature, when the environment changes too quickly that the slower evolutionary process cannot equip enough information for the population to survive, having the ability to learn during the lifetime is necessary to keep pace with the changing environment. This paper investigates the effect of learning on evolution in evolutionary optimisation. An instance of dynamic optimisation problems is proposed to test the theory. Experimental results show that learning has a significant impact on guiding evolutionary search in the dynamic landscapes. Indications for future work on dynamic optimisation are also presented.