Machine Learning Assisted Evolutionary Multi- And Many- Objective Optimization

Saxena, Dhish Kumar, Mittal, Sukrit, Deb, Kalyanmoy

Machine Learning Assisted Evolutionary Multi- And Many- Objective Optimization
  • 出版商: Springer
  • 出版日期: 2024-05-18
  • 售價: $7,010
  • 貴賓價: 9.5 折 $6,660
  • 語言: 英文
  • 頁數: 244
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819920957
  • ISBN-13: 9789819920952
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits.
Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain.

To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.


商品描述(中文翻譯)

本書專注於機器學習（ML）輔助的多目標和多目標優化（EMâO）。 EMâO算法，即EMâOAs，通過迭代演化一組解向良好的Pareto前沿逼近。 EMâOAs在連續的世代中提供多個解集，使其適用於應用ML進行不同追求的情況。

鑑於ML在EMâO領域中的巨大潛力，本書旨在成為領域新手和經驗豐富的研究人員和從業人員的專屬資源。為了實現這一目標，本書首先介紹了優化的基礎知識，包括問題和算法類型。然後，結構良好的章節系統地介紹了EMâO領域中基於ML的增強研究的一些關鍵研究，並系統地解決了重要方面。這些方面包括學習理解問題結構，更好地收斂，更好地多樣化，同時更好地收斂和多樣化，以及分析Pareto前沿。在此過程中，本書廣泛總結了文獻，從創新化工作（2003年）和目標減少（2006年）開始，延伸到最近提出的創新進展運算子（2021-23年）。它還強調了ML干預在使用EMâOAs的搜索，後優化和決策階段中的實用性。最後，本書對ML在EMâOA領域中的未來潛力提供了深入的觀點。

為了幫助讀者，本書提供了開發算法的工作代碼。本書不僅將加強這一新興主題，還鼓勵ML研究人員開發更高效和可擴展的方法，以滿足EMâOA領域的要求。它為在EMâOA和ML領域的協同交叉點進一步的研究和應用提供了靈感。

作者簡介

Dhish Kumar Saxena received the bachelor's degree in mechanical engineering (1997), the master's degree in solid mechanics and design (1999), and the Ph.D. degree in evolutionary many-objective optimization (2008) from the Indian Institute of Technology Kanpur, India. Currently, he is a Professor at the Department of Mechanical and Industrial Engineering, and a joint faculty at the Mehta Family of Data Science and Artificial Intelligence, Indian Institute of Technology (IIT) Roorkee, India. Prior to joining IIT Roorkee, he worked with the Cranfield University and Bath University, U.K., from 2008 to 2012. At a fundamental level, his research has focused on Multi- and Many-objective optimization, including, development of Evolutionary Algorithms and their performance enhancement using Machine Learning; Termination criterion for these algorithms; and Decision Support based on objectives and constraints' relative preferences. At an applied level, his focus has been on demonstrating the utility of Evolutionary and Mathematical Optimization on a range of real-world problems, including scheduling, engineering design, business-process, and multi-criterion decision making. He is also an Associate Editor for Elsevier's Swarm and Evolutionary Computation journal.

Sukrit Mittal is a Senior Research Scientist in the AI & Optimization Research team at Franklin Templeton Investments. He obtained his B.Tech. (2012-16) and Ph.D. (2018-22) degrees from IIT Roorkee, India. He also worked with Mahindra Research Valley as a design engineer (2016-18). His research has primarily focused on evolutionary multi- and many-objective optimization, machine learning assisted optimization, and innovization.

Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. His research interests are in evolutionary optimization and their application inmulti-criterion optimization, modeling, and machine learning. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in EMO, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of IEEE and ASME.

Erik D. Goodman was PI and Director of BEACON Center for the Study of Evolution in Action, an NSF Center headquartered at Michigan State University, 2010-2018. He was Professor of Electrical & Computer Engineering, also Mechanical Engineering and Computer Science & Engineering, until retiring in 2022. He co-founded Red Cedar Technology (1999, now part of Siemens), and developed the HEEDS SHERPA commercial design optimization software. Honors include Michigan Distinguished Professor of the Year, 2009; MSU Distinguished Faculty Award, 2011; Senior Fellow, International Society for Genetic and Evolutionary Computation, 2004; Founding Chair, ACM SIG on Genetic and Evolutionary Computation (SIGEVO), 2005-2007.

作者簡介(中文翻譯)

Dhish Kumar Saxena在印度卡納普爾的印度理工學院獲得機械工程學士學位（1997年），固體力學和設計碩士學位（1999年），以及進化多目標優化博士學位（2008年）。目前，他是印度羅爾基印度理工學院機械與工業工程系的教授，也是Mehta Family of Data Science and Artificial Intelligence的聯合教職。在加入印度羅爾基印度理工學院之前，他曾在英國的克蘭菲爾德大學和巴斯大學工作（2008年至2012年）。他的研究主要集中在多目標優化，包括發展進化算法及其在機器學習方面的性能提升；這些算法的終止條件；以及基於目標和約束條件相對偏好的決策支持。在應用層面上，他的重點是展示進化和數學優化在一系列實際問題上的效用，包括排程、工程設計、業務流程和多標準決策。他還是Elsevier的Swarm and Evolutionary Computation期刊的副編輯。

Sukrit Mittal是富蘭克林坦普頓投資公司AI和優化研究團隊的高級研究科學家。他在印度羅爾基印度理工學院獲得了B.Tech.（2012-16）和Ph.D.（2018-22）學位。他還曾在馬欣德拉研究谷擔任設計工程師（2016-18）。他的研究主要集中在進化多目標優化、機器學習輔助優化和創新。

Kalyanmoy Deb是密歇根州立大學電氣和計算機工程系的杰出大學教授和Koenig講座教授。他的研究興趣包括進化優化及其在多標準優化、建模和機器學習中的應用。他獲得了IEEE進化計算先驅獎、Infosys獎、TWAS工程科學獎、CajAstur Mamdani獎、Edgeworth-Pareto獎、Bhatnagar工程科學獎和德國Bessel研究獎。他是IEEE和ASME的會士。

Erik D. Goodman是BEACON Center for the Study of Evolution in Action的PI和主任，該中心是美國國家科學基金會的中心，總部設在密歇根州立大學，任期為2010年至2018年。他曾任電氣和計算機工程、機械工程和計算機科學與工程教授，直到2022年退休。他共同創辦了Red Cedar Technology（1999年，現為西門子的一部分），並開發了HEEDS SHERPA商業設計優化軟件。他的榮譽包括2009年密歇根州傑出教授年度獎、2011年MSU傑出教職獎、2004年國際遺傳和進化計算學會高級研究員、2005年至2007年ACM遺傳和進化計算SIG（SIGEVO）創始主席。

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