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Register now to contribute a chapter to ‘Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization’*

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Deadline Extended: Open for Submission

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

ISBN: 978-953-51-6070-0

Javier Del Ser Lorente

Book editor

Javier Del Ser Lorente

University of the Basque Country (UPV/EHU), Spain

Co-editor(s)

Eneko Osaba
Tecnalia Research & Innovation, Spain

Nature-inspired heuristics

Evolutionary Algorithms

Swarm Intelligence

Hyper Heuristics

Memetic Computing

About the Book

About the Book

Nature-inspired Methods
for Stochastic, Robust and Dynamic Optimization

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. In this regard many different inspirational sources can be found for these solvers, such as the behavioral patterns of bats, fireflies, corals, bees or cuckoos, as well as the mechanisms behind genetic inheritance, musical harmony composition or bacterial foraging. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics to be covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, adaptive nature-inspired techniques, hyper heuristics, memetic methods, or distributed evolutionary techniques, 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, dynamic and constraint satisfaction problems. This material unleashes a great opportunity for researchers, lecturers, and practitioners interested in nature-inspired computation, artificial intelligence and optimization problems.

Topics

Topics

The following topics illustrate the target subject areas and scope of the project.

These keywords are not definitive but can be used as the basis for the chapter content.

We accept theoretical and applied scientific papers which can be presented as original research papers and review papers. The required length of the full chapters is 14 to 20 pages.

Subject areas and keywords

Nature-inspired heuristics

Swarm Intelligence

Memetic Computing

Stochastic Optimization

Robust Optimization

Optimization under uncertainty

Evolutionary Algorithms

Hyper Heuristics

Distributed Evolutionary Techniques

Dynamic Optimization

Multimodal Optimization

Constraint Satisfaction Problems

Submit

Peer Review Process

  • Ithenticate
  • CrossRef
Chapter Proposal

1. Chapter Proposal

After accepting this invitation and registering for this project, you will be asked to submit a chapter proposal that includes a tentative title, keywords and a short topic proposal for your book chapter (100 - 150 words). You are welcome to work with co-authors, and will have the option to add them if you know already who you will be collaborating with.

Full Chapter

2. Full Chapter Review

Full chapter manuscripts are screened for plagiarism and undergo a main Editor Peer Review. Following a rigorous assessment and approval process, a review report will provide suggestions for improvements and revisions to suit overall book content. Results are sent to authors within 30 days.

Publish

Publishing with InTechOpen

InTechOpen is the world leader in Open Access book publishing. The book format allows researchers to provide a more comprehensive analysis compared to traditional journal articles. With its responsive staff, a fast review process and an impressive reader base, it is easy to see why InTechOpen has become the prefered publishing option for over 107,565 scholars.

InTechOpen regulary submits its books to major databases for evaluation and coverage, including the ISI Thomson Reuters Book Citation Index in the Web of ScienceTM Core Collection. Other discipline-specific databases are also targeted, such as Web of Science's BIOSIS Previews.

We are especially proud of our authors

Ohsumi

Yoshinori Ohsumi
2016 NOBEL WINNER

Kroto

Harold W. Kroto
1996 NOBEL WINNER

Register to collaborate on ‘Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization’

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deadlines

Important Deadlines

Registration

November 01, 2017

Deadline Extended: Open for Submission

Full chapter (14-20 pages)

January 05, 2018

Deadline Extended: Open for Submission

Expected publication date

May 2018

Nature-inspired Methods
for Stochastic, Robust and Dynamic Optimization

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