Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms

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.

本書專注於對隨機優化算法性能進行全面比較，提供了對當前用於分析算法性能的常見場景的概述，同時解決了常常被忽視的問題。同時，本書還展示了如何通過應用產生了深度統計比較及其變體的原則來輕鬆避免這些問題。重點是使用單目標和多目標優化數據進行統計分析。在書的最後，介紹了一個最近開發的基於網絡服務的電子學習工具（DSCTool）的示例。該工具為用戶提供了在各種統計場景下進行強大的統計比較分析所需的所有功能。

本書旨在為新手和有經驗的研究人員提供幫助。對於新手，本書介紹了優化和統計分析的基礎知識，使他們熟悉主題，然後介紹了深度統計比較方法。有經驗的研究人員可以快速轉到新統計方法的內容。本書分為三個部分：

第一部分：優化、基準測試和統計分析簡介 - 第2-4章。
第二部分：元啟發式隨機優化算法的深度統計比較 - 第5-7章。
第三部分：深度統計比較的實施和應用 - 第8章。

Tome Eftimov is currently a research fellow at the Jozef Stefan Institute, Ljubljana, Slovenia where he was awarded his PhD. He has since been a postdoctoral research fellow at the Dept. of Biomedical Data Science, and the Centre for Population Health Sciences, Stanford University, USA, and a research associate at the University of California, San Francisco, USA. His main areas of research include statistics, natural language processing, heuristic optimization, machine learning, and representational learning. His work related to benchmarking in computational intelligence is focused on developing more robust statistical approaches that can be used for the analysis of experimental data.

Peter Korosec received his PhD degree from the Jozef Stefan Postgraduate School, Ljubljana, Slovenia. Since 2002 he has been a researcher at the Computer Systems Department of the Jozef Stefan Institute, Ljubljana. He has participated in the organization of various conferences workshops as program chair or organizer. He has successfully applied his optimization approaches to several real-world problems in engineering. Recently, he has focused on better understanding optimization algorithms so that they can be more efficiently selected and applied to real-world problems.

The authors have presented the related tutorial at the significant related international conferences in Evolutionary Computing, including GECCO, PPSN, and SSCI.

Tome Eftimov目前是斯洛維尼亞盧布爾雅那Jozef Stefan研究所的研究員，他在該所獲得了博士學位。此後，他曾在斯坦福大學生物醫學數據科學系和人口健康科學中心擔任博士後研究員，並在加州大學舊金山分校擔任研究助理。他的主要研究領域包括統計學、自然語言處理、啟發式優化、機器學習和表徵學習。他在計算智能中與基準測試相關的工作集中在開發更強大的統計方法，用於分析實驗數據。

Peter Korosec在斯洛維尼亞盧布爾雅那的Jozef Stefan研究所計算機系統部門擔任研究員，於該所獲得博士學位。自2002年以來，他一直致力於工程領域的多個實際問題的優化研究。他曾參與組織多個會議和研討會，擔任程序主席或組織者。最近，他專注於更好地理解優化算法，以便更有效地選擇和應用於實際問題。

作者們已在進化計算的重要國際會議上，包括GECCO、PPSN和SSCI，介紹了相關的教程。