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New Frontier in Evolutionary Algorithms : Theory and Applications
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New Frontier in Evolutionary Algorithms : Theory and Applications
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New Frontiers in Evolutionary Algorithms: Theory and Applications
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Evolutionary Computation in Gene Regulatory Network Research (eng)
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Evolutionary Computation in Gene Regulatory Network Research
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Evolutionary Computation in Gene Network Research (Wiley Series in Bioinformatics)
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Evolutionary Computation in Gene Regulatory Network Research
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Evolutionary Computation in Gene Network Research (Wiley Series in Bioinformatics)
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Evolutionary Computation in Gene Regulatory Network Research
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Evolutionary Computation in Gene Regulatory Network Research
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Evolutionary Computation in Gene Regulatory Network Research
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Evolutionary Computation in Gene Regulatory Network Research
Lingua: Inglese
Editore: John Wiley and Sons Inc, US, 2016
ISBN 10: 1118911512 ISBN 13: 9781118911518
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Aggiungi al carrelloHardback. Condizione: New. Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. . Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) . Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications . Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology . Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
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Aggiungi al carrelloCondizione: Gut. Zustand: Gut | Seiten: 318 | Sprache: Englisch | Produktart: Bücher | This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Alife and AI techniques, such as memetic concepts, swarm intelligence and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting and data mining in bioinformatics. The emphasis of this book is applicability to real-world tasks. Tasks from application areas optimization of the trading rule in foreign exchange (FX) and stock prices, economic load dispatch in power system, exit/door placement for evacuation planning, and gene regulatory network inference in bioinformatics are studied, and the resultant empirical investigations demonstrate how successful the proposed approaches are when solving real-world tasks of great importance.
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Evolutionary Computation in Gene Regulatory Network Research (Wiley Series in Bioinformatics)
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Evolutionary Computation in Gene Regulatory Network Research
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
Libro 32 di 32: Natural ComputingDa: GreatBookPrices, Columbia, MD, U.S.A.
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Evolutionary Computation in Gene Regulatory Network Research
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Aggiungi al carrelloCondizione: New. Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). Series: Wiley Series in Bioinformatics. Num Pages: 464 pages, illustrations. BIC Classification: MBF; UY. Category: (P) Professional & Vocational. Dimension: 242 x 160 x 29. Weight in Grams: 760. . 2016. 1st Edition. Hardcover. . . . .
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
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Aggiungi al carrelloCondizione: New. Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GR.
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Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series)
Libro 32 di 32: Natural ComputingDa: Ria Christie Collections, Uxbridge, Regno Unito
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Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series)
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New Frontier in Evolutionary Algorithms : Theory and Applications
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New Frontier in Evolutionary Algorithms: Theory and Applications
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Evolutionary Computation in Gene Regulatory Network Research
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New Frontier in Evolutionary Algorithms : Theory and Applications
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
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Evolutionary Computation in Gene Regulatory Network Research
Da: Kennys Bookstore, Olney, MD, U.S.A.
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Condizione: New. Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). Series: Wiley Series in Bioinformatics. Num Pages: 464 pages, illustrations. BIC Classification: MBF; UY. Category: (P) Professional & Vocational. Dimension: 242 x 160 x 29. Weight in Grams: 760. . 2016. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
Libro 32 di 32: Natural ComputingDa: GreatBookPricesUK, Woodford Green, Regno Unito
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Evolutionary Computation in Gene Regulatory Network Research
Lingua: Inglese
Editore: John Wiley and Sons Inc, US, 2016
ISBN 10: 1118911512 ISBN 13: 9781118911518
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. . Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) . Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications . Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology . Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
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Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series) 1st ed. 2020 Edition
Libro 32 di 32: Natural ComputingDa: Books Puddle, New York, NY, U.S.A.
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