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Titlebook: Evolutionary and Swarm Intelligence Algorithms; Jagdish Chand Bansal,Pramod Kumar Singh,Nikhil R. Book 2019 Springer International Publis

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書目名稱Evolutionary and Swarm Intelligence Algorithms
編輯Jagdish Chand Bansal,Pramod Kumar Singh,Nikhil R.
視頻videohttp://file.papertrans.cn/319/318053/318053.mp4
概述Presents recent research in evolutionary algorithms.Includes state-of-the-art research in swarm intelligence.Written by experts in the field
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Evolutionary and Swarm Intelligence Algorithms;  Jagdish Chand Bansal,Pramod Kumar Singh,Nikhil R.  Book 2019 Springer International Publis
描述This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
出版日期Book 2019
關(guān)鍵詞Computational Intelligence; Evolutionary Algorithms; Swarm Intelligence; Evolutionary Intelligence; Swar
版次1
doihttps://doi.org/10.1007/978-3-319-91341-4
isbn_softcover978-3-030-08229-1
isbn_ebook978-3-319-91341-4Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer International Publishing AG, part of Springer Nature 2019
The information of publication is updating

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Genetic Algorithm and Its Advances in Embracing Memetics,hich are otherwise difficult to solve using classical, deterministic techniques. GAs are easier to implement as compared to many classical methods, and have thus attracted extensive attention over the last few decades. However, the inherent randomness of these algorithms often hinders convergence to
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Genetic Programming for Classification and Feature Selection,with feature selection. We begin with a brief account of how genetic programming has emerged as a major computational intelligence technique. Then, we analyse classification and feature selection problems in brief. We provide a naive model of GP-based binary classification strategy with illustrative
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