Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Sukrit Mittal, Kalyanmoy Deb, Dhish Kumar Saxena, et al.
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Naturwissenschaften, Medizin, Informatik, Technik / Informatik
Beschreibung
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.
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.
Kundenbewertungen
Evolutionary Multi-objective Optimization, Diversity Maintenance, Machine Learning, Evolutionary Computation, Convergence