Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms
暫譯: 深度統計比較於元啟發式隨機優化演算法

Eftimov, Tome, Korosec, Peter

Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms
  • 出版商: Springer
  • 出版日期: 2023-06-12
  • 售價: $6,340
  • 貴賓價: 9.5 折 $6,023
  • 語言: 英文
  • 頁數: 133
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3030969193
  • ISBN-13: 9783030969196
  • 相關分類: Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

本書專注於隨機優化演算法性能的全面比較，提供了當前用於分析演算法性能的各種常見情境的概述，同時也探討了經常被忽視的問題。進而，本書展示了如何通過應用已產生深度統計比較（Deep Statistical Comparison）及其變體的原則，輕鬆避免這些問題。本書重點在於使用單目標和多目標優化數據進行的統計分析。在書末，展示了一個最近開發的基於網路服務的電子學習工具（DSCTool）的範例。該工具為用戶提供了在各種統計情境中進行穩健統計比較分析所需的所有功能。

本書適合該領域的新手和經驗豐富的研究人員。對於新手，本書涵蓋了優化和統計分析的基本知識，使他們在介紹深度統計比較方法之前熟悉主題。經驗豐富的研究人員則可以快速進入有關新統計方法的內容。本書分為三個部分：

第一部分：優化、基準測試和統計分析介紹 - 第2至第4章。
第二部分：元啟發式隨機優化演算法的深度統計比較 - 第5至第7章。
第三部分：深度統計比較的實施與應用 - 第8章。

作者簡介

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.

作者簡介(中文翻譯)

Tome Eftimov 目前是斯洛維尼亞盧布爾雅那約瑟夫·斯特凡研究所的研究員，並在此獲得博士學位。此後，他曾在美國史丹佛大學生物醫學數據科學系及人口健康科學中心擔任博士後研究員，並在美國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。他在計算智能基準測試方面的工作專注於開發更穩健的統計方法，以用於實驗數據的分析。

Peter Korosec 於斯洛維尼亞盧布爾雅那的約瑟夫·斯特凡研究所研究生院獲得博士學位。自2002年以來，他一直是約瑟夫·斯特凡研究所計算機系統部的研究員。他參與了多個會議工作坊的組織，擔任程序主席或組織者。他成功地將其優化方法應用於多個工程中的現實問題。最近，他專注於更好地理解優化算法，以便能夠更有效地選擇和應用於現實問題。

作者在與進化計算相關的重要國際會議上展示了相關的教程，包括 GECCO、PPSN 和 SSCI。

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