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Approaches to Statistical Comparisons Used for Stochastic Optimization Algorithms,First, the most commonly used approach for a statistical comparison is presented, followed by a recently published approach, known as the Deep Statistical Comparison. Both approaches are discussed using benchmarking scenarios introduced in the statistical analysis chapter (i.e., the single-problem and multiple-problem scenarios).
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978-3-030-96919-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Developing Leadership Developmentf the optimization results. First, the optimization and its two main families in the form of combinatorial and numerical optimization are introduced. Next, the two classifications of optimization problems (i.e., single-objective and multi-objective) are defined. Finally, optimization heuristics and
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A Holistic Approach to School SuccessThe four main steps of benchmarking will be explained in more detail, starting from identifying the reasons for benchmarking, defining the optimization domain (problem and algorithm selection), defining and executing the experimental design, and analyzing the experimental results with statistical an
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A Holistic Approach to School SuccessFirst, the most commonly used approach for a statistical comparison is presented, followed by a recently published approach, known as the Deep Statistical Comparison. Both approaches are discussed using benchmarking scenarios introduced in the statistical analysis chapter (i.e., the single-problem a
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https://doi.org/10.1007/978-3-031-06916-1eep Statistical Comparison ranking scheme can be used for a performance assessment of single-objective stochastic optimization algorithms. Next, a practical Deep Statistical Comparison ranking scheme is introduced, followed by examples for testing whether the statistical significance presented in th