Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization - Dhish Kumar Saxena

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Résumé :
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....

Biographie:
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...

Sommaire:
This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EM?O). EM?O algorithms, namely EM?OAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EM?OAs amenable to application of ML for different pursuits.