Titlebook: Adaptation and Hybridization in Computational Intelligence; Iztok Fister,Iztok Fister Jr. Book 2015 Springer International Publishing Swit

起皱纹

https://doi.org/10.1007/978-3-319-14400-9Adaptation; Computational Intelligence; Cuckoo Search; Data Mining; Firefly Algorithm; Natural Selection;

EXUDE

978-3-319-35905-2Springer International Publishing Switzerland 2015

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Iztok Fister,Iztok Fister Jr.Presents recent research in self-adaptation techniques in computational intelligence algorithms and applications as well as theoretical analysis.Provides both theoretical treatments and real-world ins

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Adaptation, Learning, and Optimizationhttp://image.papertrans.cn/a/image/144528.jpg

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轻快来事

1867-4534 ysis.Provides both theoretical treatments and real-world ins.This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates backgr

有毒

Book 2015sists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI..This book c

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Book 2015are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms..

排名真古怪

AFPM Machines Without Stator Cores,n probability (..) parameters to 4.00, 4.25, 0.014, 0.225, and 0.025, respectively. This finding would eliminate the concerns regarding the optimal tuning of the KH algorithm for its most future applications.

充足

AFPM Machines Without Stator Cores,50°C and 600°C. Therefore, the use of ANN was one of the possible solutions which proved to be very promising. However, the results of this ANN with manual parameter setting by an expert can significantly be improved when using the meta-GA for automatic searching the optimal parameter setting of the original ANN algorithm.