ISBN: 9780387094267
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… mais…
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ISBN: 9780387094267
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… mais…
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2011, ISBN: 9780387094267
The UGP Toolkit, eBooks, eBook Download (PDF), Auflage, [PU: Springer-Verlag], [ED: 1], Springer-Verlag, 2011
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2011, ISBN: 9780387094267
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2011, ISBN: 9780387094267
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ISBN: 9780387094267
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… mais…
ISBN: 9780387094267
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… mais…
2011
ISBN: 9780387094267
The UGP Toolkit, eBooks, eBook Download (PDF), Auflage, [PU: Springer-Verlag], [ED: 1], Springer-Verlag, 2011
2011, ISBN: 9780387094267
eBooks, eBook Download (PDF), 2011, [PU: Springer US], Springer US, 2011
2011, ISBN: 9780387094267
2011, eBook Download (PDF), eBooks, [PU: Springer US]
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Dados detalhados do livro - Evolutionary Optimization: the µGP toolkit
EAN (ISBN-13): 9780387094267
ISBN (ISBN-10): 0387094261
Ano de publicação: 2011
Editor/Editora: Springer-Verlag
178 Páginas
Língua: eng/Englisch
Livro na base de dados desde 2009-06-09T22:42:25-03:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-06-14T07:44:41-03:00 (Sao Paulo)
Número ISBN/EAN: 9780387094267
Número ISBN - Ortografia alternativa:
0-387-09426-1, 978-0-387-09426-7
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: sanchez, massimi, pierre tan
Dados da editora
Autor: Ernesto Sanchez; Massimiliano Schillaci; Giovanni Squillero
Título: Evolutionary Optimization: the µGP toolkit
Editora: Springer; Springer US
178 Páginas
Ano de publicação: 2011-04-01
New York; NY; US
Impresso / Feito em
Língua: Inglês
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XIII, 178 p.
EA; E107; eBook; Nonbooks, PBS / 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; B; Artificial Intelligence; Computer Applications; Computer-Aided Engineering (CAD, CAE) and Design; Artificial Intelligence; Computer and Information Systems Applications; Computer-Aided Engineering (CAD, CAE) and Design; Computer Science; 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
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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