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Advanced Machine Learning with Evolutionary and Metaheuristic Techniques

  • Book
  • © 2024

Overview

Editors:
  1. Jayaraman Valadi

    1. Computing and Data Sciences, FLAME University, Pune, India

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  2. Krishna Pratap Singh

    1. Indian Institute of Information Technology Allahabad, Prayagraj, India

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  3. Muneendra Ojha

    1. Indian Institute of Information Technology Allahabad, Prayagraj, India

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  4. Patrick Siarry

    1. University Paris-Est Créteil, Créteil, France

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  • A comprehensive exploration of evolutionary and metaheuristic algorithms applied to various aspects of machine learning
  • Showcases how evolutionary and metaheuristic algorithms are revolutionizing industries like biomed and healthcare
  • Integrates different domains of AI, including evolutionary algorithms, metaheuristics, reinforcement learning, etc.

Part of the book series: Computational Intelligence Methods and Applications (CIMA)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-x
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  2. From Evolution to Intelligence: Exploring the Synergy of Optimization and Machine Learning

    • Kedar Nath Das, Rahul Paul
    Pages 1-31

  3. Metaheuristic and Evolutionary Algorithms in Explainable Artificial Intelligence

    • Hardik Prabhu, Aamod Sane, Renu Dhadwal, Patrick Siarry, Jayaraman Valadi
    Pages 33-65

  4. Evolutionary Dynamic Optimization and Machine Learning

    • Abdennour Boulesnane
    Pages 67-85

  5. Evolutionary Techniques in Making Efficient Deep-Learning Framework: A Review

    • Shubham Joshi, Millie Pant, Kusum Deep
    Pages 87-103

  6. Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization

    • Arindam Ghosh, Ojaswita Tiwari, Krishna Pratap Singh, Muneendra Ojha
    Pages 105-120

  7. Synergies Between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview

    • Ujwala Bharambe, Rekha Ramesh, Manimala Mahato, Sangita Chaudhari
    Pages 121-151

  8. Heuristics-Based Hyperparameter Tuning for Transfer Learning Algorithms

    • Upendra Pratap Singh, Krishna Pratap Singh, Muneendra Ojha
    Pages 153-184

  9. Machine Learning Applications of Evolutionary and Metaheuristic Algorithms

    • Anupam Yadav, Shrishti Chamoli
    Pages 185-211

  10. Machine Learning Assisted Metaheuristic Based Optimization of Mixed Suspension Mixed Product Removal Process

    • Ravi Kiran Inapakurthi, Sakshi S. Naik, Kishalay Mitra
    Pages 213-239

  11. Machine Learning Based Intelligent RPL Attack Detection System for IoT Networks

    • A. Kannan, M. Selvi, S. V. N. Santhosh Kumar, K. Thangaramya, S. Shalini
    Pages 241-256

  12. Shallow and Deep Evolutionary Neural Networks Applications in Solid Mechanics

    • Anna Malá, Zdeněk Padovec, Tomáš Mareš, Nirupam Chakraborti
    Pages 257-296

  13. Polymer and Nanocomposite Informatics: Recent Applications of Artificial Intelligence and Data Repositories

    • Neelesh Ashok, K. P. Soman, Madhav Samanta, M. S. Sruthi, Prabaharan Poornachandran, Suja Devi V. G et al.
    Pages 297-322

  14. Synergistic Combination of Machine Learning and Evolutionary and Heuristic Algorithms for Handling Imbalance in Biological and Biomedical Datasets

    • Sonal Modak, Mayur Pandya, Patrick Siarry, Jayaraman Valadi
    Pages 323-362
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Keywords

About this book

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.


 


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.


Editors and Affiliations

  • Computing and Data Sciences, FLAME University, Pune, India

    Jayaraman Valadi

  • Indian Institute of Information Technology Allahabad, Prayagraj, India

    Krishna Pratap Singh, Muneendra Ojha

  • University Paris-Est Créteil, Créteil, France

    Patrick Siarry

About the editors

Dr. Jayaraman Valadi  is a Distinguished Professor of Computer Science at FLAME University, Pune, India. He earned his Doctorate degree in Chemistry from Pune University. His research encompasses diverse areas, focusing on modeling and simulations in chemical and biochemical engineering, as well as process modeling, control, and optimization. Over the past decade, he has dedicated his efforts to exploring applications of Machine Learning and Artificial intelligence across various domains. He has dozens of publications in various reputed international journals. Beginning his journey in 1976, Dr. Valadi was associated with the Council of Industrial and Scientific Research (CSIR) in India, where he worked for 33 years and retired as a Deputy Director in 2009. After that, he was a CSIR Emeritus Scientist at the Center for Development of Advanced Computing, Pune till January 2013 & thereafter as a visiting faculty at Shiv Nadar University, Greater Noida, India until May 2023.


 

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.


Bibliographic Information

  • Book Title: Advanced Machine Learning with Evolutionary and Metaheuristic Techniques

  • Editors: Jayaraman Valadi, Krishna Pratap Singh, Muneendra Ojha, Patrick Siarry

  • Series Title: Computational Intelligence Methods and Applications

  • DOI: https://doi.org/10.1007/978-981-99-9718-3

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

  • Hardcover ISBN: 978-981-99-9717-6Published: 23 April 2024

  • Softcover ISBN: 978-981-99-9720-6Due: 24 May 2024

  • eBook ISBN: 978-981-99-9718-3Published: 22 April 2024

  • Series ISSN: 2510-1765

  • Series E-ISSN: 2510-1773

  • Edition Number: 1

  • Number of Pages: X, 362

  • Number of Illustrations: 1 b/w illustrations

  • Topics: Machine Learning, Health Informatics

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