2014 International Conference on Informatics, Networking and by Jiaxing Zhang

By Jiaxing Zhang

This court cases quantity includes chosen papers provided on the 2014 overseas convention on Informatics, Networking and clever Computing, held in Shenzhen, China. Contributions disguise the most recent advancements and advances within the box of Informatics, Networking and clever Computing.

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In other words, classifiers will be tuned only on this sample and will not necessary be effective working with other data. To overcome this problem and to obtain a fuzzy system of high generalized ability we used in our work the method of cross-validation. For each: from five complexes, each table of observations is divided into training and the testing selections in the ratio 80:20. Before an application, our proposed fuzzy classifier uses two different methods: a Particle Swarm Optimization algorithm (PSO) and an Ant Colony Optimization algorithm (ACO) with Single tone approximation.

This technique was proposed by Karaboga, (2005) and further improved by Karaboga & Basturk, (2008). The ABC algorithm works with a colony of artificial bees. The colony consists of three groups of bees: employed bees (workers), on lookers, and scouts. The position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution. The number of the employed bees or the onlooker bees is equal to the number of solutions in the population.

2008. Induction of Ab¯ fuzzy classification systems via evolutionary ACO-based algorithms. International Journal of Simulation, Systems, Science and Technology, 9: 1–8. , & Toro, M. 2003. Evolutionary learning of hierarchical decision rules. IEEE Transactions on Systems, Man, and Cybernetics, 33(B): 324–331. , & Karaboga, D. 2009. Solving Integer Programming Problems by Using Artificial Bee Colony Algorithm. In R. Serra and R. ) AI*IA 2009, LNAI 5883: 355–364. Berlin, Heidelberg: Springer-Verlag.

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