Bioinspired Computation in Combinatorial Optimization [recurso electrónico] : Algorithms and Their Computational Complexity / by Frank Neumann, Carsten Witt.

Por: Neumann, Frank [author.]Colaborador(es): Witt, Carsten [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Natural Computing SeriesEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: XII, 216 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642165443Tema(s): Computer science | Information theory | Computer software | Artificial intelligence | Mathematical optimization | Computer Science | Algorithm Analysis and Problem Complexity | Optimization | Theory of Computation | Artificial Intelligence (incl. Robotics)Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 005.1 Clasificación LoC:QA76.9.A43Recursos en línea: Libro electrónicoTexto
Contenidos:
Basics -- Combinatorial Optimization and Computational Complexity -- Stochastic Search Algorithms -- Analyzing Stochastic Search Algorithms -- Single-objective Optimization -- Minimum Spanning Trees -- Maximum Matchings -- Makespan Scheduling -- Shortest Paths -- Eulerian Cycles -- Multi-objective Optimization -- Multi-objective Minimum Spanning Trees -- Minimum Spanning Trees Made Easier -- Covering Problems -- Cutting Problems.
En: Springer eBooksResumen: Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area. The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems. This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity.  
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Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos QA76.9 .A43 (Browse shelf(Abre debajo)) 1 No para préstamo 375338-2001

Basics -- Combinatorial Optimization and Computational Complexity -- Stochastic Search Algorithms -- Analyzing Stochastic Search Algorithms -- Single-objective Optimization -- Minimum Spanning Trees -- Maximum Matchings -- Makespan Scheduling -- Shortest Paths -- Eulerian Cycles -- Multi-objective Optimization -- Multi-objective Minimum Spanning Trees -- Minimum Spanning Trees Made Easier -- Covering Problems -- Cutting Problems.

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area. The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems. This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity.  

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