Get Intelligent Computing Theory: 10th International Conference, PDF

By De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne

This publication – along with the volumes LNAI 8589 and LNBI 8590 – constitutes the refereed court cases of the tenth foreign convention on clever Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The ninety two papers of this quantity have been conscientiously reviewed and chosen from various submissions. The papers are equipped in topical sections similar to evolutionary computation and studying; swarm intelligence and optimization; computing device studying; social and ordinary computing; neural networks; biometrics popularity; photograph processing; details safety; digital truth and human-computer interplay; wisdom discovery and information mining; sign processing; trend acceptance; biometric method and safeguard for clever computing.

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Additional info for Intelligent Computing Theory: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings

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Comparison of PSO-TVAC, SADE, EAM and GEHA on Rotated Bent Cigar for minimum value on different dimensions It is found that the minimum value obtained by GEHA is very less as compared to other algorithms for all dimensions with population size 40 and 100 for Shifted Rastrigin function. GEHA has given good results for the population size 40 as compared to the population size 100. Fig. 5. Comparison of PSO-TVAC, SADE, EAM and GEHA on Shifted Rastrigin for minimum value on different dimensions For Shifted Rosenbrock function the minimum value obtained by SADE, EAM and GEHA are looking similar up to the dimension number 20.

200103YB136). References 1. : Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4(2), pp. 1942–1948 (1995) 2. : Convergence and Runtime Analysis of ITO Algorithm for One Class of Combinatorial Optimization. Chinese Journal of Computers 34(4), 636–646 (2011) (in Chinese) 3. : A New Evolutionary Algorithms for Global Numerical Optimization Based on Ito Process. , Liu, Y. ) ISICA 2010. CCIS, vol. 107, pp. 57–67. Springer, Heidelberg (2010) 4. : BBOB-benchmarking: A new evolutionary algorithms inspired by ITO process for noiseless function tested.

Merging the Ranking and Selection into ITO Algorithm for Simulation Optimization. , Liu, Y. ) ISICA 2010. CCIS, vol. 107, pp. 87–96. Springer, Heidelberg (2010) 7. : Simulation Optimization Based on the Hypothesis Testing and ITO Process. In: Third International Conference on Natural Computation, pp. 1210–1221 (2007) 8. : Improved ITO Algorithm for Solving the CVRP. Computer Science 40(5), 213–216 (2013) (in Chinese) 9. : The Simulation Optimization Algorithm Based on the Ito Process. , Loog, M.

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