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Introduction To Stochastic Search And Optimization

real-world problems. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction;

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Optimization Under Stochastic Uncertainty

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease;

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Optimization Under Stochastic Uncertainty

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease;

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Stochastic Dynamics, Filtering and Optimization

introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes;

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Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

Stochastic global optimization methods and applications to chemical, biochemical, pharmaceutical and environmental processes presents;

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Intelligent Control

processes is to combine the strengths of both Lyapunov theory/H theory-based local search methods and stochastic optimization-based global search;

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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms

of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All;

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Archiving Strategies for Evolutionary Multi objective Optimization Algorithms

of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All;

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Bayesian and High-Dimensional Global Optimization

of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods;

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Portfolio Optimization with Different Information Flow

Portfolio Optimization with Different Information Flow recalls the stochastic tools and results concerning the stochastic;

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Introduction to Linear Organization and Extensions With MATLAB

, Introduction to Linear Optimization and Extensions with MATLAB (R) provides a concrete and intuitive yet rigorous introduction to modern linear;

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Adaptive Stochastic Optimization Techniques with Applications

optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic;

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Optimization in Engineering Sciences

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems;

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General-Purpose Optimization Through Information Maximization

introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings;

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General-Purpose Optimization Through Information Maximization

introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings;

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Stochastic Process Optimization using Aspen Plus (R)

) Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization;

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Stochastic Process Optimization using Aspen Plus (R)

exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the;

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Stochastic Optimization with Simulation Based Optimization

Stochastic optimization is vital to making sound engineering and business decisions under uncertainty. While the limited capability;

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Stochastic Global Optimization

global optimization is applicable to any type of problems having non-differentiable functions, discrete variables and/or continuous variables. It;

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Introduction to Applied Optimization

optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control;

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Introduction to Applied Optimization

optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control;

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Stochastic Optimization Methods: Applications in Engineering and Operations Research

stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the;

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Electromagnetic Optimization By Genetic Algorithms

design and optimization of various low- and high-frequency electromagnetic components. Special features include: Introduction by David E;

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Stochastic Analysis and Applications

This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and;

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Stochastic Optimization

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic;

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Stochastic Analysis and Applications

This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and;

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Reactive Search and Intelligent Optimization

Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt;

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