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Experimental Methods for the Analysis of Optimization Algorithms

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Cover of 'Experimental Methods for the Analysis of Optimization Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 The Future of Experimental Research
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    Chapter 3 Design and Analysis of Computational Experiments: Overview
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    Chapter 4 Experimental Methods for the Analysis of Optimization Algorithms
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    Chapter 5 The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison
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    Chapter 6 Algorithm Engineering: Concepts and Practice
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    Chapter 7 Algorithm Survival Analysis
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    Chapter 8 On Applications of Extreme Value Theory in Optimization
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    Chapter 9 Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization
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    Chapter 10 Mixed Models for the Analysis of Optimization Algorithms
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    Chapter 11 Tuning an Algorithm Using Design of Experiments
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    Chapter 12 Using Entropy for Parameter Analysis of Evolutionary Algorithms
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    Chapter 13 F-Race and Iterated F-Race : An Overview
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    Chapter 14 The Sequential Parameter Optimization Toolbox
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    Chapter 15 Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches
Attention for Chapter 8: On Applications of Extreme Value Theory in Optimization
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