Titlebook: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms; Tome Eftimov,Peter Korošec Book 2022 The Editor(s) (if

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Book 2022es used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statis

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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms978-3-030-96917-2Series ISSN 1619-7127 Series E-ISSN 2627-6461

expository

https://doi.org/10.1007/978-90-481-9106-2timization algorithm with the performances of other, state-of-the-art algorithms. Additionally, there is a brief explanation of all the chapters to enable the reader to become acquainted with the scientific content of the book.

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GRAIN

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https://doi.org/10.1007/978-3-031-06916-1k. We give an overview of the basic terms used in statistics, starting with descriptive statistics and a special focus on hypothesis testing. At the end, we provide guidelines for which statistical test should be selected, depending on the benchmarking scenario that is analyzed.

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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 and multiple-problem scenarios).

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