Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization

ISBN-13: 9789819920952 / Angielski

Dhish Kumar Saxena; Kalyanmoy Deb; Erik D. Goodman

Opis

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 novice and the experienced researchers and practitioners. Towards it, first the foundations of optimization (problem and algorithm types) are covered. Then, some of the key studies on ML based enahancements in the EMâO domain are presented through well structured chapters which systematically narrate important aspects, including, 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, starting with the foundational work on innovization (2003) and objective reduction (2006), up to the most recently proposed innovized progress operators (2021- 23); and 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. For the benefit of the readers, the working codes of the developed algorithms are also available along with the book. 

This book will not only strengthen this emergent theme, it may also encourage the ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. This book shall inspire more research and applications across the synergistic intersection of EMâOA and ML domains.

Spis treści

Introduction.- Optimization Problems and Algorithms.- Existing Machine Learning Studies on Multi-objective Optimization.- Learning to Converge Better and Faster.- Learning to Diversify Better and Faster.- Learning to Simultaneously Converge and Diversify Better and Faster.- Learning to Understand the Problem Structure.- ML-Assisted Analysis of Pareto-optimal Front.- Further Machine Learning Assisted Enhancements.- Conclusions.

O autorze

Dhish Kumar Saxena is an Associate Professor at the Department of Mechanical & Industrial Engineering and Mehta Family School of Data Science and Artificial Intelligence, at the Indian Institute of Technology Roorkee. Prior to joining IIT Roorkee, Dhish worked with Cranfield University and Bath University, UK (2008-12). His research has focused on development of: evolutionary multi- and many-objective optimization algorithms; their performance enhancement through machine learning; their termination criterion; and decision support based on redundancy determination and preference ranking of objectives and constraints. He is also an Associate Editor for Elsevier’s Swarm and Evolutionary Computation journal.

Sukrit Mittal is a Quant Development Specialist in the AI & Digital Transformation team at Franklin Templeton. He obtained his B.Tech (2012-16) and Ph.D (2018-22) degrees from IIT Roorkee. He also worked with Mahindra Research Valley as an 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 in multi-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. He has published over 575 research papers with over 180,000 citations (Google Scholar) and h-index 131. 

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.

Blurb

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.