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The so-called Baldwin Effect generally says how learning, as a form ofontogenetic adaptation, can influence the process of phylogenetic adaptation,or evolution. This idea has also been taken into computation in which evolutionand learning are used as computational metaphors, including evolving neuralnetworks. This paper presents a technique called evolving self-taught neuralnetworks - neural networks that can teach themselves without externalsupervision or reward. The self-taught neural network is intrinsicallymotivated. Moreover, the self-taught neural network is the product of theinterplay between evolution and learning. We simulate a multi-agent system inwhich neural networks are used to control autonomous agents. These agents haveto forage for resources and compete for their own survival. Experimentalresults show that the interaction between evolution and the ability to teachoneself in self-taught neural networks outperform evolution and self-teachingalone. More specifically, the emergence of an intelligent foraging strategy isalso demonstrated through that interaction. Indications for future work onevolving neural networks are also presented.

Evolving Self-taught Neural Networks: The Baldwin Effect and the Emergence of Intelligence