Overview
- Dedicated to machine learning based performance enhancements in evolutionary multi- and many objective optimization
- Discusses the topics in a clear and structured manner, covering the search, post-optimality, and decision making phases
- Written by leading researchers in the field
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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About this book
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.
Keywords
Table of contents (10 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Authors: Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-981-99-2096-9
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-99-2095-2Published: 18 May 2024
Softcover ISBN: 978-981-99-2098-3Due: 01 June 2025
eBook ISBN: 978-981-99-2096-9Published: 17 May 2024
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XV, 244
Number of Illustrations: 30 b/w illustrations, 53 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Computational Intelligence