Evolutionary and swarm intelligence algorithms [electronic resource] / edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal.
- 其他作者：
- 其他題名：
- Studies in computational intelligence ;
- 出版： Cham : Springer International Publishing :Imprint: Springer 2019.
- 叢書名： Studies in computational intelligence,v.779
- 主題： Swarm intelligence. , Evolutionary computation. , Computational Intelligence. , Artificial Intelligence.
- ISBN： 9783319913414 (electronic bk.) 、 9783319913391 (paper)
- URL：
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- 一般註：Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems. E1084學校採購電子書
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讀者標籤：
- 系統號： 000274723 | 機讀編目格式
館藏資訊
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.
摘要註
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.