By De-Shuang Huang, Kang Li, George William Irwin

ISBN-10: 3540372776

ISBN-13: 9783540372776

This publication constitutes the refereed court cases of the foreign convention on clever Computing, ICIC 2006, held in Kunming, China, in August 2006.

The one hundred sixty five revised complete papers have been rigorously reviewed and chosen from over 3000 submissions. The papers are geared up in topical sections on ant colony optimization, particle swarm optimization, swarm intelligence, autonomy-oriented computing, quantum and molecular computations, organic and DNA computing, clever computing in bioinformatics, clever computing in computational biology and drug layout, computational genomics and proteomics, in addition to man made lifestyles and synthetic immune structures in clever computing. additionally there are 2 specified classes on bio-oriented and bio-inspired details structures, in addition to novel functions of information discovery on bioinformatics.

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Additional info for Computational Intelligence and Bioinformatics: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006, Proceedings,

Example text

In the test instances, AWACS also performs much better than ACS. With the help of tuning methodologies, it is possible to find the best performing parameter settings in a very reasonable computational time. And the argument given in this paper that the adaptive setting can save time instead of choosing the β value experimentally is convincing as the numerical results show. It is uncertain theoretically that the adaptive setting of AWACS is the best. Further study is suggested to explore the better management for the optimal setting of α β and other parameters, which will be very helpful in the application of ACO algorithms.

5-6, (2003)443-451 10. , Gambardella, L. IEEE Trans. Evol. Comput, Vol. 1, (1997)53–66 11. Luca Maria Gambardella, Marco Dorigo: Ant-Q: A Reinforcement Learning Approach to theTraveling Salesman Problem. ICML (1995) 252-260 12. Marco Dorigo , Gianni Di Caro: Ant Colony Optimization: A New Meta-Heuristic. Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, Volume: 2, 69 July 1999 13. , Maniezzo, V, Trubian, M: Ant System for Job-Shop Scheduling. Belgian Journal of Operations Research, Statistics and Computer Science, (1993)39-54 14.

In addition, the pheromone trails on every path will decay with time. If a path has not been added pheromone for a certain time, its pheromone density will reduce to zero. The decay of pheromone quality will also help ants to find the shorter path quicker for the pheromone on the longer paths receive less pheromone and their faster decay of pheromone will make it less attractive to ants. 24 X. Luo, F. Yu, and J. Zhang Fig. 1. Example of pheromone evolution in the nature. The real ants can find the shorter path.

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Computational Intelligence and Bioinformatics: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006, Proceedings, by De-Shuang Huang, Kang Li, George William Irwin


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