Download Advances in Computational Intelligence: IEEE World Congress by Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, PDF

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 concentrate on the development in evolutionary computation, in neural networks, and in fuzzy platforms. The e-book offers the services and stories of top researchers spanning a various spectrum of computational intelligence in those parts. the result's a balanced contribution to the examine sector of computational intelligence that are supposed to serve the neighborhood not just as a survey and a reference, but in addition as an proposal for the long run development of the state-of-the-art 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.

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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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Extra resources for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures

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38 R. Miikkulainen et al. The playing field contains one central obstacle (a wall), four peripheral obstacles (trees), and four walls around the perimeter to contain all agents in the same general area. Each NERO agent starts a battle with 20 hit-points. At each time slice of the simulation, each agent has the opportunity to fire a virtual laser at the closest target on the opponent’s team that is within two degrees of the agent’s current orientation. If an agent fires and hits an opponent, the opponent loses one hit-point.

One group of near-duplicate teams was submitted to the tournament; this shows up as the band of similar-colored games about one-third of the way through the matrix. The teams in the Figure are enumerated on both axes in order of increasing average match score differential. 40 R. Miikkulainen et al. Table 1. pop 130 lolwutamidoing 126 me - Rambo 126 PollusPirata 125 Cyber-trout 124 CirclingBullies 123 SneakySnipers 121 Tut 121 coward1 120 Table 1 shows the top ten teams. Despite the large number of teams, no single competitor emerged that significantly outperformed all others.

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. : Memory-based approaches to approximating continuous functions. , Eubank, S. ) Nonlinear Modeling and Forecasting, pp. 503–521. Addison Wesley, Harlow (1992) 29. : Locally Weighted Learning. Artificial Intelligence Review 11(1-5), 11–73 (1997) 30. : Is readability compatible with accuracy? From neuro-fuzzy to lazy learning.

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