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

Mihir Narayan Mohanty (u. a.)

ISBN 10: 3031117697 / ISBN 13: 9783031117695

Published by Springer, 2023

About this Item

Meta Heuristic Techniques in Software Engineering and Its Applications | METASOFT 2022 | Mihir Narayan Mohanty (u. a.) | Taschenbuch | Artificial Intelligence-Enhanced Software and Systems Engineering | x | Englisch | 2023 | Springer | EAN 9783031117695 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 122019952

Bibliographic Details

• Title: Meta Heuristic Techniques in Software Engineering and Its Applications | METASOFT 2022
• Publisher: Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 3031117697
• ISBN 13: 9783031117695
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 20D millimeters 235 millimeters width by 155 millimeters height by 20 millimeters depth
• Weight: 557 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.