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Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022

Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022

Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT
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Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022 Hardback - 2022

by Mohanty, Mihir Narayan (Edited by)/ Das, Swagatam (Edited by)/ Ray, Mitrabinda (Edited by)/ Patra, Bichitrananda (Edited by)

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Springer, 2022. Hardcover. New. 368 pages. 9.25x6.10x1.02 inches.
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Details

  • Title Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022
  • Author Mohanty, Mihir Narayan (Edited by)/ Das, Swagatam (Edited by)/ Ray, Mitrabinda (Edited by)/ Patra, Bichitrananda (Edited by)
  • Binding Hardback
  • Condition New
  • Pages 358
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2022
  • Bookseller's Inventory # __3031117123
  • ISBN 9783031117121 / 3031117123
  • Weight 1.58 lbs (0.72 kg)
  • Category Technology & Industrial Arts
  • Quantity available 1

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Reader reviews for Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022

From the publisher

This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.

From the rear cover

This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.

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