Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Javier Del Ser Lorente

ISBN 10: 1789233283 / ISBN 13: 9781789233285

Published by Intechopen Jul 2018, 2018

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems. 70 pp. Englisch.

Seller Inventory # 9781789233285

Bibliographic Details

• Title: Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization
• Publisher: Intechopen Jul 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 1789233283
• ISBN 13: 9781789233285
• Binding: Buch
• Condition: Neu
• Dimensions: 266W x 185H x 10D millimeters 266 millimeters width by 185 millimeters height by 10 millimeters depth
• Weight: 376 grams

About this title

Synopsis

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems.