Textbook in PDF format

This book explains what is required to make a more robust statistical comparison of the performance achieved by meta-heuristics. It does not explain how to develop a new single- or multi-objective meta-heuristic, or how to run it on a specific or a set of problem instances and collect the experimental data. It deals in which statistical analysis should be made once the experimental data is collected to obtain robust statistical outcomes.
Introduction
Meta-heuristic Stochastic Optimization
Benchmarking Theory
Introduction to Statistical Analysis
Approaches to Statistical Comparisons Used for Stochastic Optimization Algorithms
Deep Statistical Comparison in Multi-Objective Optimization
Deep Statistical Comparison in Single-Objective Optimization
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