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Evolutionary and Swarm Intelligence Algorithms 2019 ed. [Hardback]

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  • Formāts: Hardback, 190 pages, height x width: 235x155 mm, weight: 3013 g, 21 Illustrations, color; 36 Illustrations, black and white; X, 190 p. 57 illus., 21 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 779
  • Izdošanas datums: 15-Jun-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319913395
  • ISBN-13: 9783319913391
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 127,23 €*
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  • Standarta cena: 149,69 €
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  • Formāts: Hardback, 190 pages, height x width: 235x155 mm, weight: 3013 g, 21 Illustrations, color; 36 Illustrations, black and white; X, 190 p. 57 illus., 21 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 779
  • Izdošanas datums: 15-Jun-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319913395
  • ISBN-13: 9783319913391
Citas grāmatas par šo tēmu:
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.
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.