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Spatially Structured Evolutionary Algorithms : Artificial Evolution in Space and Time / by Marco Tomassini.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Natural Computing SeriesEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Descripción: xiii, 193 páginas recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783540299387
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QA75.5-76.95
Recursos en línea:
Contenidos:
Setting the Stage for Structured Populations -- Island Models -- Island Models: Empirical Properties -- Lattice Cellular Models -- Lattice Cellular Models: Empirical Properties -- Random and Irregular Cellular Populations -- Coevolutionary Structured Models -- Some Nonconventional Models.
Resumen: Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.
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Springer eBooks

Setting the Stage for Structured Populations -- Island Models -- Island Models: Empirical Properties -- Lattice Cellular Models -- Lattice Cellular Models: Empirical Properties -- Random and Irregular Cellular Populations -- Coevolutionary Structured Models -- Some Nonconventional Models.

Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.

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