By Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel Al-Jumaily (auth.), Marco Dorigo, Mauro Birattari, Christian Blum, Maurice Clerc, Thomas Stützle, Alan F. T. Winfield (eds.)
This publication constitutes the refereed court cases of the sixth foreign Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2008, held in Brussels, Belgium, in September 2008.
The 17 revised complete papers, 24 revised brief papers, and 10 prolonged abstracts provided have been rigorously reviewed and chosen from ninety one submissions. The papers conceal theoretical and foundational points of computational intelligence and comparable disciplines with distinctive specialize in swarm intelligence and are dedicated to behavioral versions of social bugs and new algorithmic techniques, empirical and theoretical examine in swarm intelligence, functions comparable to ant colony optimization or particle swarm optimization, and theoretical and experimental examine in swarm robotics systems.
Read or Download Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings PDF
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Extra info for Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings
The closer cf is to 1, the higher is the probability to produce the solution T rb (or T bs analogously). 2 for compiling the software. Our experimental results were obtained on a PC with an AMD64X2 4400 processor and 4 GB of memory. We used the same set of benchmark instances as in [12,13]. This set consists of 30 problem instances with 20 nodes each, and further 30 problem instances with 50 nodes. Results for Omni-Directional Antennas. First, we conducted tuning experiments concerning parameter rmax of the VND algorithm (see Alg.
C. H. Moore Concept Diﬃculty Combining the diﬃculty of modeling nonlinear attribute interactions with the challenge of attribute selection yields for this domain what Goldberg  calls a needle-in-a-haystack problem. That is, there may be a particular combination of SNPs that together with the right nonlinear function are a signiﬁcant predictor of disease susceptibility. Considered individually they may not look any diﬀerent than thousands of other SNPs not involved in the disease process. Under these models, the learning algorithm is truly looking for a genetic needle in a genomic haystack.
Of ACM MobiHoc 2002, pp. 112–122. ACM press, New York (2002) 36 H. Hern´ andez, C. Blum, and G. Franc`es 7. : On minimum-energy broadcasting in all-wireless networks. In: Proc. of IEEE LCN, pp. 14–16. IEEE press, Los Alamitos (2001) 8. : A dynamic multicast tree reconstruction algorithm for minimum-energy multicasting in wireless ad hoc networks. In: Proc. of IEEE IPCCC, pp. 637–642. IEEE press, Los Alamitos (2004) 9. : r-shrink: A heuristic for improving minimum power broadcast trees in wireless networks.