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Engineering - Computational Intelligence and Complexity | Ordinal Optimization - Soft Optimization for Hard Problems

Ordinal Optimization

Soft Optimization for Hard Problems

Ho, Yu-Chi, Zhao, Qian-Chuan, Jia, Qing-Shan

2007, XV, 317 p.

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  • Examines the difficulties of simulation-based optimization problems
  • Identifies the distinct advantages of the ordinal approach for search-based type problems
  • The tools described in this book can be used separately or in conjunction with other methodological tools of optimization
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. 

Content Level » Research

Keywords » Alignment - Kolmogorov equivalence - Performance - Simulation - complex simulation models - exponential convergence - optimization - ordinal optimization - search based methods - universal alignment

Related subjects » Computational Intelligence and Complexity - Electronics & Electrical Engineering - Operations Research & Decision Theory - Robotics

Table of contents 

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.

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