By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)
This state of the art survey bargains a renewed and clean specialize in the growth in evolutionary computation, in neural networks, and in fuzzy structures. The e-book provides the services and stories of top researchers spanning a various spectrum of computational intelligence in those components. the result's a balanced contribution to the examine quarter of computational intelligence that are meant to serve the group not just as a survey and a reference, but additionally as an notion for the longer term development of the state-of-the-art of the sector. The thirteen chosen chapters originate from lectures and shows given on the IEEE international Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Read Online 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 info for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
Communications of the ACM 18(9), 509–517 (1975) 39. : N-Body Problems in Statistical Learning. In: Proc. Advances in Neural Information Processing Systems, NIPS (2001) 40. : Management of Complex Dynamic Systems based on Model-Predictive Multi-objective Optimization. In: CIMSA 2006, La Coruña, Spain, pp. 64–69 (2006) 41. : A review of two industrial deployments of multi-criteria decision-making systems at General Electric. edu Abstract. Neuroevolution is a promising approach for constructing intelligent agents in many complex tasks such as games, robotics, and decision making.
It turns out that it is often easier to coevolve components that cooperate to form a solution, rather than evolve the complete solution directly [15,26]. The components will thus evolve diﬀerent roles in the cooperative task. For example, in the Enforced SubPopulations (ESP) architecture , neurons selected from diﬀerent subpopulations are required to form a neural network whose ﬁtness is then shared equally among them. Such an approach breaks a complex task into easier sub-tasks, avoids competing conventions among the component neurons and makes the search space smaller.
Fig. 5. Monocultural agents learning from the entire population and subcultural agents learning only from their subcultures. Subcultural agents outperform monocultural agents, converging to a much higher ultimate ﬁtness. The system utilizes a steady-state evolution in which at every timestep each agent probabilistically teaches the lowest-ﬁtness member of the population within some radius, eﬀectively forming geographical subcultures. Figures 6 and 7 show the results of the subcultural ESL algorithm compared to the student-teacher variant of NEW TIES and simple neuroevolution.