
Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
Description
It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization andmachine learning, paving the way for pioneering innovations in the field.
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Persons
Dr. Krishna Pratap Singh is an Associate Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, where he also heads the Machine Learning and Optimization (MLO) Lab. Dr. Singh earned his Ph.D. in Optimization (2009) from IIT Roorkee, and has over 15 years of research and academic experience. He is a member of the Sakura Science Club, Japan, Senior member IEEE and ACM Member. Currently, his research group is working on Transfer Learning for low resources data and towards developing a model in a Federated learning setting.
Dr. Muneendra Ojha is an Assistant Professor in the Department of Information Technology at the Indian Institute of Information Technology Allahabad (IIITA), India, and leading the Artificial Intelligence and Multiagent Systems (AIMS) lab. Dr. Ojha earned his Ph.D. from IIITA and MS from the University of Missouri-Columbia, USA.Dr. Ojha has more than 19 years of academic and industry experience. His research interests include multi-objective optimization, evolutionary algorithms, semantic web, natural language processing, deep reinforcement learning, and multi-agent systems.
Dr. Patrick Siarry received the PhD degree from the University Paris 6, in 1986 and the Doctorate of Sciences(Habilitation) from the University of Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (EDF. Since 1995 he is a full Professor of automatics and informatics. His main research interests are the adaptation of new stochastic global optimization heuristics to various situations (multi objective mixed discrete-continuous variables, continuous variables, dynamic,etc.) and their application to various engineering fields. He is also interested in the fitting of process models to experimental data and thelearning of fuzzy rule bases and neural networks. P.Siarry is a senior member IEEE, an appointed member of the Technical Committee on Soft Computing of the IEEE systems, Man and Cybernetics (SMC) Society and an appointed member of the Technical Committee on Optimal Control (TC 2.4) of IFAC.
Content
Chapter 1. From Evolution to Intelligence: Exploring the Synergy of Optimization and Machine Learning.- Chapter 2. Metaheuristic and Evolutionary Algorithms in Ex-plainable Artificial Intelligence.- Chapter 3. Evolutionary Dynamic Optimization and Machine Learning.- Chapter 4. Evolutionary Techniques in making Efficient Deep-Learning Framework: A Review.- Chapter 5. Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization.- Chapter 6. Synergies between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview.- Chapter 7. Heuristics-based Hyperparameter Tuning for Transfer Learning Algorithms.- Chapter 8. Machine Learning Applications of Evolutionary and Metaheuristic Algorithms.- Chapter 9. Machine Learning Assisted Metaheuristic Based Optimization of Mixed Suspension Mixed Product Removal Process.- Chapter 10. Machine Learning based Intelligent RPL Attack Detection System for IoT Networks.- Chapter 11. Shallow and Deep Evolutionary Neural Networks applications in Solid Mechanics.- Chapter 12. Polymer and nanocomposite Informatics: Recent Applications of Artificial Intelligence and Data Repositories.- Chapter 13. Synergistic combination of machine learning and evolutionary and heuristic algorithms for handling imbalance in biological and biomedical datasets.