Abstract <p>In the present work, a model of the interaction between learning and evolution at the formation of functional systems is constructed and studied. The behavior of a population of learning agents is analyzed. The agent’s control system consists of a set of functional systems. Each functional system includes a set of elements. The presence or absence of an element in the considered functional system is encoded by binary symbols 1 or 0. Each agent has a genotype and phenotype, which are encoded by chains of binary symbols and represent the combined chains of functional systems. A functional system is completely formed when all its elements are present in it. The more is the number of completely formed functional systems that an agent has, the higher is the agent’s fitness. The evolution of a population of agents consists of generations. During each generation, the genotypes of agents do not change, and the phenotypes are optimized via learning, namely, via the formation of new functional systems. The phenotype of an agent at the beginning of a generation is equal to its genotype. At the end of the generation, the number of functional systems in the agent’s phenotype is determined; the larger is this number, the higher is the agent’s fitness. Agents are selected into a new generation with probabilities that are proportional to their fitness. The descendant agent receives the genotype of the parent agent (with small mutations). Thus, the selection of agents occurs in accordance with their phenotypes, which are optimized by learning, and the genotypes of agents are inherited. The model was studied by computer simulation; the effects of the interaction between learning and evolution in the processes of formation of functional systems were analyzed.</p>