Natural computing algorithms
Por: Brabazon, Anthony
Editor: Estados Unidos de América Springer 2015Descripción: 554 Páginas 24 18ISBN: 9783662436318Tema(s): Computer science | Operations research | Decision making | Artificial intelligence | Management scienceClasificación CDD: 004.015 Tipo de ítem | Ubicación actual | Signatura | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras |
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Bibl. Central de Ingenierías | A-98-01-0005 (Navegar estantería) | 1 | Disponible | Primer Piso | BCI/M19016-1 |
Introduction -- Introduction to Evolutionary Computing -- Genetic Algorithms -- Extending the Genetic Algorithm -- Evolution Strategies and Evolutionary Programming -- Differential Evolution -- Genetic Programming -- Particle Swarm Algorithms -- Ant Algorithms -- Honeybee Algorithms -- Other Social Algorithms -- Bacterial Foraging Algorithms -- Neural Networks for Supervised Learning -- Neural Networks for Unsupervised Learning -- Neuroevolution -- Artificial Immune Systems -- An Introduction to Developmental and Grammatical Computing -- Grammar-Based and Developmental Genetic Programming -- Grammatical Evolution -- TAG3P and Developmental TAG3P -- Genetic Regulatory Networks -- An Introduction to Physics-Inspired Computing -- Physics-Inspired Computing Algorithms -- Quantum-Inspired Evolutionary Algorithms -- Plant-Inspired Algorithms -- Chemistry-Inspired Algorithms -- Conclusions -- References -- Index.
The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design. This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.
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