By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)
This cutting-edge survey deals a renewed and clean concentrate on the development in evolutionary computation, in neural networks, and in fuzzy structures. The ebook provides the services and stories of prime researchers spanning a various spectrum of computational intelligence in those parts. the result's a balanced contribution to the study region of computational intelligence that are meant to serve the neighborhood not just as a survey and a reference, but additionally as an idea for the long run development of the cutting-edge of the sector. The thirteen chosen chapters originate from lectures and displays given on the IEEE global Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Read or Download Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures PDF
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Additional resources for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
Neural Networks 5, 241–259 (1992) 23. org/wiki/Meta_learning_(computer_science) 24. : Meta-learning: searching in the model space. In: Proc. of the Int. Conf. on Neural Information Processing (ICONIP), Shanghai, China (2001) 25. : Meta-learning. Scholarpedia 5(6), 4650 (2010) 26. : Shift of bias for inductive concept learning. , Mitchell, T. ) Machine Learning, pp. 163–190 (1986) 27. : Evolutionary principles in self-referential learning. Diploma thesis, Institut für Informatik, Technische Universität München (1987) 28.
Four of these sense one of the prey agents. Their outputs are given to a ﬁfth combiner network that outputs the next move for that predator. Each network is evolved in a separate ESP process, where one subpopulation is evolved for each of the neurons in the network (a). The predator is evaluated in the domain simulation with prey and other predator agents (c). Its ﬁtness is distributed equally among all the networks and among all the neurons that participated in it. In this manner, evolution can discover neurons and networks that cooperate well to form an eﬀective agent.
Evolution 7(2), 110–117 (1953) 39. : Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002) 40. : Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation 9(6), 653– 668 (2005) 46 R. Miikkulainen et al. 41. : Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation 9(6), 653– 668 (2005) 42. : Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998) 43. : Distributed computing in practice: the condor experience.