Machine Learning Assisted Evolutionary Multi- And Many- Objective Optimization

This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EM槄). EM槄 algorithms, namely EM槄As, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EM槄As amenable to application of ML, for different pursuits.
Recognizing the immense potential for ML-based enhancements in the EM槄 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 EM槄 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 EM槄As. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EM槄A 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 EM槄A domain. This book shall inspire more research and applications across the synergistic intersection of EM槄A and ML domains.