Metaheuristics for Finding Multiple Solutions
暫譯: 多解的元啟發式演算法

Preuss, Mike, Epitropakis, Michael G., Li, Xiaodong

Metaheuristics for Finding Multiple Solutions
  • 出版商: Springer
  • 出版日期: 2021-10-23
  • 售價: $7,920
  • 貴賓價: 9.5 折 $7,524
  • 語言: 英文
  • 頁數: 241
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030795527
  • ISBN-13: 9783030795528

    • 海外代購書籍(需單獨結帳)

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商品描述

This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are "multimodal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate several such solutions before deciding which one to use. Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades. These multimodal optimization techniques are commonly referred to as "niching" methods, because of the nature-inspired "niching" effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc. Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges.

To facilitate the advance of niching methods in facing these challenges, this edited book highlights the latest developments in niching methods. The included chapters touch on algorithmic improvements and developments, representation, and visualization issues, as well as new research directions, such as preference incorporation in decision making and new application areas. This edited book is a first of this kind specifically on the topic of niching techniques.

This book will serve as a valuable reference book both for researchers and practitioners. Although chapters are written in a mutually independent way, Chapter 1 will help novice readers get an overview of the field. It describes the development of the field and its current state and provides a comparative analysis of the IEEE CEC and ACM GECCO niching competitions of recent years, followed by a collection of open research questions and possible research directions that may be tackled in the future.

商品描述(中文翻譯)

這本書介紹了多模態優化和細分技術的最新趨勢和發展。大多數現有的優化方法是為了尋找單一的全局解決方案而設計的。然而，在現實世界中，許多問題本質上是「多模態」的，即存在多個滿意的解決方案。在決定使用哪一個之前，尋找幾個這樣的解決方案可能是理想的。過去幾十年來，多模態優化一直是基於群體的元啟發式算法領域的研究重點，例如進化算法（EAs）。這些多模態優化技術通常被稱為「細分」方法，因為它們引入了自然啟發的「細分」效應，旨在針對多個最優解的解決方案群體。EAs 社群中已經開發了許多細分方法。一些經典的例子包括擁擠、適應度共享、清除、降級、限制性錦標賽選擇、物種形成等。然而，將這些細分方法應用於現實世界的多模態問題時，往往會遇到重大挑戰。

為了促進細分方法在面對這些挑戰時的進展，這本編輯書籍突出了細分方法的最新發展。所包含的章節涉及算法改進和發展、表示和可視化問題，以及新的研究方向，例如在決策中納入偏好和新的應用領域。這本編輯書籍是專門針對細分技術主題的首本此類書籍。

這本書將成為研究人員和實務工作者的寶貴參考書。雖然各章節是以相互獨立的方式撰寫，但第一章將幫助初學者了解該領域的概況。它描述了該領域的發展及其當前狀態，並提供了對近年來 IEEE CEC 和 ACM GECCO 細分競賽的比較分析，隨後列出了一系列未來可能解決的開放研究問題和研究方向。

作者簡介

Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with the information systems institute of WWU Muenster, Germany (headquarter of ERCIS), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence methods for computer games, and their successful application to real-world problems as chemical retrosynthesis.

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from the University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor in the School of Science (Computer Science and Software Engineering) of RMIT University, Melbourne, Australia. His research interests include evolutionary computation, machine learning, data analytics, multiobjective optimization, dynamic optimization, multimodal optimization, large-scale optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and the International Journal of Swarm Intelligence Research. He is a founding member of the IEEE CIS Task Force on Swarm Intelligence, and a former Chair of the IEEE CIS Task Force on Large-Scale Global Optimization. He is currently a Vice-chair of the IEEE CIS Task Force on Multi-Modal Optimization. He is the recipient of the 2013 SIGEVO Impact Award and the 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".

Michael G. Epitropakis received his B.S., M.Sc., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a director of technical products in The Signal Group, Athens, Greece. Previously, he was an Assistant Professor in Data Science at Lancaster University, Lancaster, UK. His current research interests include operations research, computational intelligence, evolutionary computation, swarm intelligence, multi-modal optimization, machine learning, and search-based software engineering. He is a founding member of the IEEE CIS Task Force on Multi-Modal Optimization acting as Chair/Co-Chair from its foundation.

Jonathan E. Fieldsend is Professor of Computational Intelligence at the University of Exeter. He has a BA degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and on the interface between the two. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Forces on Multi-Modal Optimization, and on Data-Driven Evolutionary Optimization of Expensive Problems. He also sits on the IEEE CIS Task Force on Evolutionary Many-Objective Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.

作者簡介(中文翻譯)

邁克·普魯斯（Mike Preuss）是荷蘭萊頓大學計算機科學研究所（LIACS）的助理教授。之前，他曾在德國明斯特大學（WWU Muenster）的資訊系統研究所工作（ERCIS的總部），並且在德國多特蒙德工業大學（TU Dortmund）的演算法工程系任職，於2013年獲得博士學位。他的研究興趣集中在針對實值問題的進化演算法領域，特別是多模態和多目標優化，以及計算智能方法在電腦遊戲中的應用，並成功應用於現實世界問題，如化學逆合成。

李曉東（Xiaodong Li）在中國西安的西電大學獲得學士學位，並在新西蘭達尼丁的奧塔哥大學獲得資訊科學的博士學位。目前，他是澳大利亞墨爾本RMIT大學科學學院（計算機科學與軟體工程）的全職教授。他的研究興趣包括進化計算、機器學習、數據分析、多目標優化、動態優化、多模態優化、大規模優化和群體智能。他擔任IEEE進化計算學報、群體智能（Springer）和國際群體智能研究期刊的副編輯。他是IEEE CIS群體智能工作組的創始成員，並曾擔任IEEE CIS大規模全局優化工作組的主席。目前，他是IEEE CIS多模態優化工作組的副主席。他是2013年SIGEVO影響獎和2017年IEEE CIS「IEEE進化計算學報傑出論文獎」的獲得者。

邁克爾·G·埃皮特羅帕基斯（Michael G. Epitropakis）在希臘帕特雷斯大學數學系獲得學士、碩士和博士學位。目前，他是希臘雅典的信號集團（The Signal Group）技術產品總監。之前，他曾在英國蘭卡斯特大學擔任數據科學助理教授。他目前的研究興趣包括運籌學、計算智能、進化計算、群體智能、多模態優化、機器學習和基於搜索的軟體工程。他是IEEE CIS多模態優化工作組的創始成員，自成立以來擔任主席/聯合主席。

喬納森·E·菲爾登（Jonathan E. Fieldsend）是英國埃克塞特大學的計算智能教授。他擁有達勒姆大學的經濟學學士學位、普利茅斯大學的計算智能碩士學位和埃克塞特大學的計算機科學博士學位。他在進化計算和機器學習領域及其交界處發表了超過100篇同行評審的論文。他是IEEE計算智能學會（CIS）多模態優化工作組和針對昂貴問題的數據驅動進化優化工作組的副主席。他還是IEEE CIS進化多目標優化工作組的成員。他是IEEE計算智能學會和ACM SIGEVO的成員。

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