Download Computational Intelligence and Intelligent Systems: 4th by Zhechao Gao, Jia Wu, Hongwu Chen, Bowen Pan, Hao Zheng, PDF

By Zhechao Gao, Jia Wu, Hongwu Chen, Bowen Pan, Hao Zheng, Dingwen Liang, Xiaoming Liu (auth.), Zhihua Cai, Zhenhua Li, Zhuo Kang, Yong Liu (eds.)

Volumes CCIS fifty one and LNCS 5812 represent the court cases of the Fourth Interational Symposium on Intelligence Computation and functions, ISICA 2009, held in Huangshi, China, in the course of October 23-25.

ISICA 2009 attracted over three hundred submissions. via rigorous stories, fifty eight papers have been incorporated in LNCS 5821,and fifty four papers have been amassed in CCIS fifty one. ISICA meetings are one of many first sequence of overseas meetings on computational intelligence that mix parts of studying, model, evolution and fuzzy good judgment to create courses as substitute suggestions to man made intelligence.

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Extra resources for Computational Intelligence and Intelligent Systems: 4th International Symposium, ISICA 2009, Huangshi, China, October 23-25, 2009. Proceedings

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Cai et al. ): ISICA 2009, CCIS 51, pp. 45–55, 2009. © Springer-Verlag Berlin Heidelberg 2009 46 J. Zhu, G. Dai, and L. Mo the population was clustered into a number of subpopulations and the PCA technique was used to generate new solutions. Based on the study and analysis of the above algorithms, this paper proposed a new algorithm, which has the following features: a) The orthogonal design method is used to generate an initial population. It makes the initial individuals scatter evenly in the feasible solution space.

N , −∞ < ai < bi < +∞ . , xn ) ∈ R n is an n-dimension decision variable vector in decision space. F ( x ) consists of m objective functions is a m-dimension objective m variable vector in objective space R . m . The set of all the Pareto optimal points is called the Pareto set, denoted by PS. The set of all the Pareto objective vectors is called the Pareto front, denoted by PF, where PF = {F ( x ) | x ∈ PS } . Multi-objective genetic algorithm is designed to construct a set of Pareto optimal solutions to approximate the Pareto front and/or Pareto set in every single run.

The tradeoff between additional overhead and security increment should be considered to improve the overall performance. For the missioncritical WSNs, the security always occupies the priority comparing with the overhead. 4 Conclusion and Future Work We presented a bio-inspired security framework - MMASec - by making use of multiple mobile agents to detect and defend sophisticated adversaries in missioncritical WSNs. The cognitive model and intelligent algorithms are selected concerning the constraints of the sensor nodes.

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