Download e-book for kindle: Ant Colony Optimization and Swarm Intelligence: 6th by Rami N. Khushaba, Ahmed Al-Ani, Akram AlSukker, Adel

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.)

ISBN-10: 3540875263

ISBN-13: 9783540875260

ISBN-10: 3540875271

ISBN-13: 9783540875277

This e-book constitutes the refereed complaints 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 awarded have been conscientiously reviewed and chosen from ninety one submissions. The papers hide theoretical and foundational features of computational intelligence and comparable disciplines with particular specialise in swarm intelligence and are dedicated to behavioral versions of social bugs and new algorithmic techniques, empirical and theoretical study in swarm intelligence, purposes corresponding to ant colony optimization or particle swarm optimization, and theoretical and experimental examine in swarm robotics systems.

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Read Online 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 resources for Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings

Example text

When the accuracy of the classifier is identical, ants that choose SNPs with better expert knowledge will contribute more pheromone to those paths than SNPs with lower expert knowledge scores. 1 Implementation This ACO is implemented in C++. For the purposes of this power analysis solutions consist of pairs of attributes. The solution kept as the result is the pair of attributes with the highest balanced accuracy according to MDR (detailed in section 3). 2 Pheromone Updating with Expert Knowledge We have discovered in our work with genetic programming that expert knowledge is critical for machine learning algorithms to be successful with this problem [7].

Concerning ACO we provide the following information. The column with heading best gives the value of the best solution found in 30 trials, whereas the column titled deviation provides the improvement over DBIP + VND (in percent). Furthermore, the column saving shows how much emission power (in percent) could be saved in comparison to the case of omni-directional antennas. The remaining columns provide the average of the best solutions found in 30 trials (+ standard deviation), and the average of the computation times over 30 trials (+ standard deviation).

332–341. IEEE press, Los Alamitos (2005) 12. : Nested partitioning for the minimum energy broadcast. In: Proc. of LION 2007. Springer, Berlin (2007) 13. : Evolutionary local search for the minimum energy broadcast problem. , Cotta, C. ) EvoCOP 2008. LNCS, vol. 4972, pp. 61–72. Springer, Heidelberg (2008) 14. : An adaptive localized scheme for energy-efficient broadcasting in ad hoc networks with directional antennas. H. ) PWC 2004. LNCS, vol. 3260, pp. 399–413. Springer, Heidelberg (2004) 15. : Improving energy efficiency for multicasting in ad-hoc networks with directional antennas.

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Ant Colony Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings 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.)


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