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Ordinal Optimization [recurso electrónico] : Soft Optimization for Hard Problems / by Yu-Chi Ho, Qian-Chuan Zhao, Qing-Shan Jia.

Por: Colaborador(es): Tipo de material: TextoTextoEditor: Boston, MA : Springer US, 2007Descripción: XV, 317 p. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • recurso en línea
ISBN:
  • 9780387686929
  • 99780387686929
Tema(s): Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD:
  • 519 23
Recursos en línea:
Contenidos:
Ordinal Optimization Fundamentals -- Comparison of Selection Rules -- Vector Ordinal Optimization -- Constrained Ordinal Optimization -- Memory Limited Strategy Optimization -- Additional Extensions of the OO Methodology -- Real World Application Examples.
En: Springer eBooksResumen: Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succint mathematical models. The purpose of this book is to address the difficulties of the optimization of complex systems via simulation models or other computation-intensive models involving possible stochastic effects and discrete choices. This book establishes distinct advantages of the "softer" ordinal approach for search-based type problems, analyzes its general properties, and shows the many orders of magnitude improvement in computational efficiency that is possible.
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Libros electrónicos Libros electrónicos CICY Libro electrónico Libro electrónico 519 (Browse shelf(Opens below)) Available

Ordinal Optimization Fundamentals -- Comparison of Selection Rules -- Vector Ordinal Optimization -- Constrained Ordinal Optimization -- Memory Limited Strategy Optimization -- Additional Extensions of the OO Methodology -- Real World Application Examples.

Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succint mathematical models. The purpose of this book is to address the difficulties of the optimization of complex systems via simulation models or other computation-intensive models involving possible stochastic effects and discrete choices. This book establishes distinct advantages of the "softer" ordinal approach for search-based type problems, analyzes its general properties, and shows the many orders of magnitude improvement in computational efficiency that is possible.

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