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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms

Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms

Korosec, Peter
Eftimov, Tome

Hardcover

Series: Natural Computing

General ComputersProbability & Statistics

ISBN10: 3030969169
ISBN13: 9783030969165
Publisher: Springer Nature
Published: Jun 12 2022
Pages: 133
Weight: 0.86
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English
Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches 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 statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios.

The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts:

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