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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 t… Mehr…

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Evolutionary Optimization: the µGP toolkit - neues Buch

2014

ISBN: 1489993681

2011 Kartoniert / Broschiert Informationstechnik (IT), allgemeine Themen, Künstliche Intelligenz, EvolutionaryOptimization; geneticprogramming; graph-basedrepresentation; industrialprob… Mehr…

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Sanchez, Ernesto; Schillaci, Massimiliano; Squillero, Giovanni:
Evolutionary Optimization: the µGP toolkit - neues Buch

2014, ISBN: 1489993681

2011 Kartoniert / Broschiert, mit Schutzumschlag neu, [PU:Springer US; Springer US, New York, N.Y.]

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Details zum Buch

Detailangaben zum Buch - Evolutionary Optimization: The Ugp Toolkit


EAN (ISBN-13): 9781489993687
ISBN (ISBN-10): 1489993681
Taschenbuch
Erscheinungsjahr: 2014
Herausgeber: Springer-Verlag New York Inc.

Buch in der Datenbank seit 2014-10-10T00:01:57+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-06-14T12:44:45+02:00 (Berlin)
ISBN/EAN: 1489993681

ISBN - alternative Schreibweisen:
1-4899-9368-1, 978-1-4899-9368-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: ernes, sanchez, massimi, giovanni


Daten vom Verlag:

Autor/in: Ernesto Sanchez; Massimiliano Schillaci; Giovanni Squillero
Titel: Evolutionary Optimization: the µGP toolkit
Verlag: Springer; Springer US
178 Seiten
Erscheinungsjahr: 2014-08-15
New York; NY; US
Gedruckt / Hergestellt in Niederlande.
Gewicht: 0,454 kg
Sprache: Englisch
112,34 € (DE)
115,49 € (AT)
124,00 CHF (CH)
POD
XIII, 178 p.

BC; Artificial Intelligence; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Evolutionary Optimization; Genetic Programming; Graph-based representation; Industrial Problems; MicroGP; Problem Complexity; Real-world Applications; Turing-complete; evolutionary algorithm; evolutionary computation; Computer Applications; Computer-Aided Engineering (CAD, CAE) and Design; Artificial Intelligence; Computer and Information Systems Applications; Computer-Aided Engineering (CAD, CAE) and Design; Angewandte Informatik; Computer-Aided Design (CAD); BB

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/

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

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)

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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