Authors:
Describes an award-winning evolutionary algorithm used for solving practical problems in industry
Provides a practical guide on using the µGP, a set of examples to clarify the available choices and advice against common errors and misconceptions
Offers practical knowledge about applying various evolutionary schemes using the toolkit, and a set of useful rules of thumb for tuning all toolkit capabilities
Includes supplementary material: sn.pub/extras
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Table of contents (12 chapters)
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Front Matter
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Back Matter
About this book
This book describes an award-winning evolutionary algorithm that outperformed experts and conventional heuristics in solving several industrial problems. It presents a discussion of the theoretical and practical aspects that enabled μGP (MicroGP) to autonomously find the optimal solution of hard problems, handling highly structured data, such as full-fledged assembly programs, with functions and interrupt handlers.
For a practitioner, μGP is simply a versatile optimizer to tackle most problems with limited setup effort. The book is valuable for all who require heuristic problem-solving methodologies, such as engineers dealing with verification and test of electronic circuits; or researchers working in robotics and mobile communication. Examples are provided to guide the reader through the process, from problem definition to gathering results.
For an evolutionary computation researcher, μGP may be regarded as a platform where new operators and strategies can be easily tested.
MicroGP (the toolkit) is an active project hosted by Sourceforge: http://ugp3.sourceforge.net/
Reviews
From the reviews:
“The text is a handbook for µGP. It is aimed at providing the reader with all of the information required for proficient use of the tool. … At the end of the book, a chapter presents a number of examples and applications. … the book meets its main objective of being a reference for the µGP tool … . It is effective as a starting guide for using the tool, and is also useful for discovering advanced features and exploiting the flexibility of individual representation.” (Corrado Mencar, ACM Computing Reviews, March, 2012)
Authors and Affiliations
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Dip. Automatica e Informatica, Politecnico di Torino, Torino, Italy
Ernesto Sanchez, Giovanni Squillero
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ICT Consultant, Torino, Italy
Massimiliano Schillaci
Bibliographic Information
Book Title: Evolutionary Optimization: the µGP toolkit
Authors: Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
DOI: https://doi.org/10.1007/978-0-387-09426-7
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media, LLC 2011
Hardcover ISBN: 978-0-387-09425-0Published: 07 April 2011
Softcover ISBN: 978-1-4899-9368-7Published: 15 August 2014
eBook ISBN: 978-0-387-09426-7Published: 01 April 2011
Edition Number: 1
Number of Pages: XIII, 178
Topics: Artificial Intelligence, Computer Applications, Computer-Aided Engineering (CAD, CAE) and Design