The cascade-correlation learning architecture
Advances in neural information processing systems 2
Artificial Life II
Specialization Under Social Conditions in Shared Environments
Proceedings of the Third European Conference on Advances in Artificial Life
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions between organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had an improved performance as compared with ASL-guided CasCor and RL-guided FuzGa. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA (food availability), reducing to an isolated solution at a lower value of FA.