Download Computational Intelligence and Intelligent Systems: 7th by Kangshun Li, Jin Li, Yong Liu, Aniello Castiglione PDF

By Kangshun Li, Jin Li, Yong Liu, Aniello Castiglione

This e-book constitutes the refereed complaints of the seventh foreign Symposium on Intelligence Computation and purposes, ISICA 2015, held in Guangzhou, China, in November 2015.

The seventy seven revised complete papers provided have been rigorously reviewed and chosen from 189 submissions. The papers characteristic the main updated study in research and concept of evolutionary computation, neural community architectures and studying; neuro-dynamics and neuro-engineering; fuzzy common sense and regulate; collective intelligence and hybrid structures; deep studying; wisdom discovery; studying and reasoning.

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Additional resources for Computational Intelligence and Intelligent Systems: 7th International Symposium, ISICA 2015, Guangzhou, China, November 21-22, 2015, Revised Selected Papers

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Results achieved by FA, VSSFA, MFA, and NFA on the test suite. 02E − 10 20 H. Wang et al. 21 ( f4) Fig. 1. The search processes of FA, VSSFA, MFA, and NFA on four selected functions. Firefly Algorithm with Local Search for Numerical Optimization 21 VSSFA could hardly achieve reasonable solutions on all test functions. Even on simple unimodal function Sphere (f1), they still cannot converge to promising solutions. Compared to FA and VSSFA, MFA and NFA obtains much better solutions on all test functions.

12(3), 304–316 (2003) 10. : Research on automatic detection and recognition technology of automobile hub defect based on X-ray images. R. com Abstract. When the traditional genetic algorithm was used to solve the community detection problem, it was not easy to avoid the problems of low efficiency and slow convergent speed. To be aim at these problems, a improved genetic algorithm which is based on the immune mechanism was proposed in this paper. In this new algorithm, the immune mechanism was used to ensure the diversity of population.

Wiley, Hoboken (2010) 2. : Binary real coded firefly algorithm for solving unit commitment problem. Inf. Sci. 249, 67–84 (2013) 3. : Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build. 59, 273–278 (2013) 4. : Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2013) 5. : Multimodal size, shape, and topology optimisation of truss structures using the firefly algorithm. Adv. Eng. Softw. 56, 23–37 (2013) 6.

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