Evolutionary Optimization: the µGP toolkit

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This text begins with an overview of the most popular techniques of evolutionary computation followed by a discussion of the theoretical and practical aspects of miGP. It includes several detailed examples with actual results.

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 GP (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, GP 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, GP 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/


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

Inhalt

Evolutionary computation.- Why yet another one evolutionary optimizer?.- The GP 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

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Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09781489993687
    • Auflage 2011
    • Sprache Englisch
    • Größe H235mm x B155mm x T11mm
    • Jahr 2014
    • EAN 9781489993687
    • Format Kartonierter Einband
    • ISBN 1489993681
    • Veröffentlichung 15.08.2014
    • Titel Evolutionary Optimization: the µGP toolkit
    • Autor Ernesto Sanchez , Giovanni Squillero , Massimiliano Schillaci
    • Gewicht 306g
    • Herausgeber Springer US
    • Anzahl Seiten 196
    • Lesemotiv Verstehen
    • Genre Informatik

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