Sanchez / Schillaci / Squillero Evolutionary Optimization: the µGP toolkit

1. Auflage 2011
ISBN: 978-0-387-09426-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 178 Seiten, eBook

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/

Zielgruppe

Research

Autoren/Hrsg.

Sanchez, Ernesto

Schillaci, Massimiliano

Squillero, Giovanni

Inhaltsverzeichnis

Evolutionary computation.- Why yet another one evolutionary optimizer?.- The µGP architecture.- Advanced features.- Performing an evolutionary run.- Command line syntax.- Syntax of the settings file.- Syntax of the population parameters file.- Syntax of the external constraints file.- Writing a compliant evaluator.- Implementation details.- Examples and applications.- Argument and option synopsis.- External constraints synopsis.- Index.- References