The Effect of Evolution in Artificial Life Learning Behavior

In this paper, we add learning behavior to artificial evolution simulation and evaluate the effect of learning behavior. Each individual establishes its own neural network with its genetic information. Also, we propose a reward function to take reinforcement learning in a complicated and dynamically-determined environment. When the individual-level learning behavior was introduced, evolution of each simulation model got faster and the effectiveness of evolution was significantly improved. But the direction of evolution did not depend on learning and it was possible to affect the forms of evolution through reinforcement learning. This provides the mechanism that can apply the artificial life technique to various fields.