04709nam a22005895i 4500 978-981-99-2096-9 DE-He213 20251030112047.0 cr nn 008mamaa 240517s2024 si | s |||| 0|eng d 9789819920969 978-981-99-2096-9 10.1007/978-981-99-2096-9 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Saxena, Dhish Kumar. author. aut http://id.loc.gov/vocabulary/relators/aut 76975 Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization [electronic resource] / by Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XV, 244 p. 83 illus., 53 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Genetic and Evolutionary Computation, 1932-0175 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. 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. Artificial intelligence. 58441 Machine learning. 55059 Computational intelligence. 64052 Artificial Intelligence. 58441 Machine Learning. 55059 Computational Intelligence. 64052 Mittal, Sukrit. author. aut http://id.loc.gov/vocabulary/relators/aut 76976 Deb, Kalyanmoy. author. aut http://id.loc.gov/vocabulary/relators/aut 76977 Goodman, Erik D. author. aut http://id.loc.gov/vocabulary/relators/aut 76978 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819920952 Printed edition: 9789819920976 Printed edition: 9789819920983 Genetic and Evolutionary Computation, 1932-0175 66950 https://doi.org/10.1007/978-981-99-2096-9 ZDB-2-SCS ZDB-2-SXCS 59934 59934