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 specialize in the development in evolutionary computation, in neural networks, and in fuzzy structures. The booklet provides the services and stories of major researchers spanning a various spectrum of computational intelligence in those components. the result's a balanced contribution to the study sector of computational intelligence that are meant to serve the neighborhood not just as a survey and a reference, but in addition as an thought for the long run development of the state-of-the-art of the sector. The thirteen chosen chapters originate from lectures and shows given on the IEEE global 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
WCCI 2012 Plenary/Invited Lectures, LNCS 7311, pp. 24–46, 2012. c Springer-Verlag Berlin Heidelberg 2012 Multiagent Learning through Neuroevolution 25 in three of them: Setting up evolution so that eﬀective collaboration emerges, combining evolution with learning within the team, and evaluating the team behaviors quantitatively. First, how should evolution be set up to promote eﬀective team behaviors. That is, when the team is successful, should the rewards be distributed among team members equally, or should individuals be rewarded for their own performance?
G. agents may need to slow down in order to avoid overshooting a plant). Each agent is controlled by an artiﬁcial neural network that maps from the agent’s sensor readings to the desired change in orientation and velocity. Two separate conﬁgurations of the robot foraging world are used in the experiments. The ﬁrst two experiments use a “simple” world where the toroidal surface is 2000 by 2000 units, with a single plant type of value 100 and 50 randomly distributed instances of the plant. In this world, the agents have a straightforward task of learning to navigate eﬃciently and gather as many plants as possible.
Oxford University Press, USA (1989) 6. : Collective behavior of interacting self-propelled particles. Physica A 281, 17–29 (2000) 7. : Incremental social learning in particle swarms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(2), 368–384 (2011) 8. : Cultural evolution in a population of neural networks. , Tagliaferri, R. ) Neural Nets Wirn 1996, pp. 100–111. Springer, Newyork (1996) 9. : Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996) 10.