We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider an agent-broker that predicts stock price changes and uses its predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either evolution or learning, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz. originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. We also compare the behavioral tactics of our agents to the searching behavior of simple animals.
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