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Learning has been shown to be beneficial to in creating more adaptive algorithms, and also in evolving neural networks. Moreover, learning can be classified into two types, namely social learning, or learning from others (e.g., imitation), and individual trial-and-error learning. A “social learning strategy” – a rule governing whether and when to use social or individual learning, is often said to be more beneficial than relying on social or individual learning alone. In this paper we compare the effect on evolution of social learning in comparison with that of individual learning. A neural architecture called a “self-taught neural network” is proposed in order to allow an agent to learn on its own, without any supervision. We simulate a multi-agent system in which agents, each controlled by a neural network, have to develop adaptive behaviour and compete with each other for survival. Experimental results show that …