Genetic Programming Theory and Practice VIII [recurso electrónico] / edited by Rick Riolo, Trent McConaghy, Ekaterina Vladislavleva.
Tipo de material:
TextoSeries Genetic and Evolutionary Computation ; 8Editor: New York, NY : Springer New York, 2011Descripción: XXVIII, 248 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441977472Tema(s): Computer science | Information theory | Computer software | Electronic data processing | Artificial intelligence | Computer Science | Computing Methodologies | Artificial Intelligence (incl. Robotics) | Theory of Computation | Algorithm Analysis and Problem Complexity | Programming TechniquesFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006 Clasificación LoC:QA75.5-76.95Recursos en línea: Libro electrónico
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | QA75.5 -76.95 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 372014-2001 |
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| QA75.5 -76.95 Comparative E-Government | QA75.5 -76.95 The P=NP Question and Gödel’s Lost Letter | QA75.5 -76.95 Information Retrieval Architecture and Algorithms | QA75.5 -76.95 Genetic Programming Theory and Practice VIII | QA75.5 -76.95 Serious Games and Edutainment Applications | QA75.5 -76.95 Research and Development in Intelligent Systems XXVIII | QA75.5 -76.95 Computability and Complexity Theory |
FINCH: A System for Evolving Java (Bytecode) -- Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems -- The Rubik Cube and GP Temporal Sequence Learning: An Initial Study -- Ensemble Classifiers: AdaBoost and Orthogonal Evolution of Teams -- Covariant Tarpeian Method for Bloat Control in Genetic Programming -- A Survey of Self Modifying Cartesian Genetic Programming -- Abstract Expression Grammar Symbolic Regression -- Age-Fitness Pareto Optimization -- Scalable Symbolic Regression by Continuous Evolution with Very Small Populations -- Symbolic Density Models of One-in-a-Billion Statistical Tails via Importance Sampling and Genetic Programming -- Genetic Programming Transforms in Linear Regression Situations -- Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis -- Composition of Music and Financial Strategies via Genetic Programming -- Evolutionary Art Using Summed Multi-Objective Ranks.
The contributions in this volume are written by the foremost international researchers and practitioners in the GP arena. They examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. Topics include: FINCH: A System for Evolving Java, Practical Autoconstructive Evolution, The Rubik Cube and GP Temporal Sequence Learning, Ensemble classifiers: AdaBoost and Orthogonal Evolution of Teams, Self-modifying Cartesian GP, Abstract Expression Grammar Symbolic Regression, Age-Fitness Pareto Optimization, Scalable Symbolic Regression by Continuous Evolution, Symbolic Density Models, GP Transforms in Linear Regression Situations, Protein Interactions in a Computational Evolution System, Composition of Music and Financial Strategies via GP, and Evolutionary Art Using Summed Multi-Objective Ranks. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results in GP .
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