Learning and evolution in neural networks
Adaptive Behavior
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolving Neural Network Ensembles by Minimization of Mutual Information
International Journal of Hybrid Intelligent Systems
Landscapes, learning costs, and genetic assimilation
Evolutionary Computation
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Evolving multilayer feedforward neural network using adaptive particle swarm algorithm
International Journal of Hybrid Intelligent Systems
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Complex organisms exhibit both evolved instincts and experiential learning as adaptive mechanisms. In isolation, neither mechanism is sufficient to successfully navigate the environments of such organisms. Instincts provide behaviors that are generally adaptive but fail in specific cases. Learning must rely on some internal or external guidance to succeed on challenging tasks. This paper explores how instincts and experiential learning can work in tandem to solve a maze environment. Specifically, instincts comprise general knowledge of a set of related mazes representing worlds that an organism might be born into, and experiential learning discriminates specific situations in the particular maze world that an organism is born into. Synergy is accomplished by a hybrid neural network, one part instinctive and the other part capable of learning. After sufficient discriminating experiences, learning can override instinct to navigate a maze when instinct would otherwise fail. Results show a marked improvement in performance when this synergistic approach is employed relative to using either instincts or learning in isolation.