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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Hardcover, 1st ed. 2022)
Loot Price: R3,932
Discovery Miles 39 320
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms (Hardcover, 1st ed. 2022)
Series: Natural Computing Series
Expected to ship within 12 - 17 working days
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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: Part I:
Introduction to optimization, benchmarking, and statistical
analysis - Chapters 2-4. Part II: Deep Statistical Comparison of
meta-heuristic stochastic optimization algorithms - Chapters 5-7.
Part III: Implementation and application of Deep Statistical
Comparison - Chapter 8.
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