LamBaDa: an artificial environment to study the interaction between evolution and learning

The study of evolutionary processes presents a major challenge due to its physical and temporal scales. Artificial life systems allow the realization of experiments concerning evolution that overcome these constraints. One aspect of the evolution of species that has been widely discussed is the role played by learning in the evolutionary process. We developed an artificial environment, LamBaDa, whose main purpose is the experimental study of interactions between learning in individual agents and evolution of populations. Agents have an internal state and a neural network that can empower them with learning faculties through a reinforcement learning algorithm. The modeling of the evolution of populations is achieved through genetic mechanisms applied during the reproduction process to the neural network weights. In this paper we describe LamBaDa, its architecture and dynamics. We present the simulation settings and discuss the results obtained, with special emphasis on the comparison of populations of agents with and without learning capabilities. The analysis of the results we obtained shows that populations of agents with learning capabilities are in advantage when compared to populations where agents can not learn, even though learned characteristics are not genetically codified. We also observed that this advantage is significant if the agents lived long enough to learn anything useful!