Autonomous Robots 7, 89–113 (1999) c  1999 Kluwer Academic Publishers. Manufactured in The Netherlands. Learning and Evolution STEFANO NOLFI Institute of Psychology, National Research Council, Viale Marx 15, Roma, Italy nolfi@ip.rm.cnr.it DARIO FLOREANO Laboratory of Microcomputing (LAMI), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland Dario.Floreano@epfl.ch Abstract. In the last few years several researchers have resorted to artificial evolution (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two different purposes: (a) looking at the performance advantages obtained by combining these two adaptive techniques; (b) understanding the role of the interaction between learning and evolution in biological organisms. In this paper we describe some of the most representative experiments conducted in this area and point out their implications for both perspectives outlined above. Understanding the interaction between learning and evolution is probably one of the best examples in which computational studies have shed light on problems that are difficult to study with the research tools employed by evolutionary biology and biology in general. From an engineering point of view, the most relevant results are those showing that adaptation in dynamic environments gains a significant advantage by the combination of evolution and learning. These studies also show that the interaction between learning and evolution deeply alters the evolutionary and the learning process themselves, offering new perspectives from a biological point of view. The study of learning within an evolutionary perspective is still in its infancy and in the forthcoming years it will produce an enormous impact on our understanding of how learning and evolution operate. Keywords: learning, evolution, plastic individuals, Baldwin effect 1. Introduction Evolution and learning are two forms of biological adaptation that differ in space and time. Evolution is a process of selective reproduction and substitution based on the existence of a geographically-distributed population of individuals displaying some variabil- ity. Learning, instead, is a set of modifications tak- ing place within each single individual during its own life time. Evolution and learning operate on different time scales. Evolution is a form of adaptation capa- ble of capturing relatively slow environmental changes that might encompass several generations, such as perceptual characteristics of food sources for a given bird species. Learning, instead, allows an individual to adapt to environmental changes that are unpredictable at the generational level. Learning might include a va- riety of mechanisms that produce adaptive changes in an individual during its lifetime, such as physical de- velopment, neural maturation, and synaptic plasticity. Finally, whereas evolution operates on the genotype, learning affects only the phenotype and phenotypic changes cannot directly modify the genotype. In the last few years researchers have used arti- ficial evolution techniques (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for the study of the interaction between learning and evolution. These studies have been conducted with two different purposes: (a) looking at the advantages, in terms of performance, that the interaction gives to evolution;