Open access peer-reviewed Edited volume

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Co-editor:

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.

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Nature-inspired Methods for Stochastic, Robust and Dynamic OptimizationEdited by Javier Del Ser Lorente

Published: July 18th 2018

DOI: 10.5772/intechopen.71401

ISBN: 978-1-78923-329-2

Print ISBN: 978-1-78923-328-5

Copyright year: 2018

Books open for chapter submissions

205 Total Chapter Downloads

chaptersDownloads

Open access peer-reviewed

1. Introductory Chapter: Nature-Inspired Methods for Stochastic, Robust, and Dynamic Optimization

By Eneko Osaba and Javier Del Ser

48

Open access peer-reviewed

2. Robust Optimization: Concepts and Applications

By José García and Alvaro Peña

61

Open access peer-reviewed

3. Evaluation of Non-Parametric Selection Mechanisms in Evolutionary Computation: A Case Study for the Machine Scheduling Problem

By Maxim A. Dulebenets

43

Open access peer-reviewed

4. A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems

By Siew Mooi Lim and Kuan Yew Leong

53

Edited volume and chapters are indexed in

  • Worldcat
  • OpenAIRE
  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI
  • IET Inspec

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