Advanced Machine Learning With Evolutionary And Metaheuristic Techniques
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Advanced Machine Learning With Evolutionary And Metaheuristic Techniques
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Author : Jayaraman Valadi
language : en
Publisher: Springer Nature
Release Date : 2024-04-22
Advanced Machine Learning With Evolutionary And Metaheuristic Techniques written by Jayaraman Valadi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-22 with Mathematics categories.
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.
Metaheuristics In Machine Learning Theory And Applications
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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2021-07-13
Metaheuristics In Machine Learning Theory And Applications written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Computers categories.
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Metaheuristic And Machine Learning Optimization Strategies For Complex Systems
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Author : R., Thanigaivelan
language : en
Publisher: IGI Global
Release Date : 2024-07-17
Metaheuristic And Machine Learning Optimization Strategies For Complex Systems written by R., Thanigaivelan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-17 with Computers categories.
In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.
Evolutionary Machine Learning Techniques
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Author : Seyedali Mirjalili
language : en
Publisher: Springer Nature
Release Date : 2019-11-11
Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Technology & Engineering categories.
This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Hybrid Metaheuristics Research And Applications
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Author : Siddhartha Bhattacharyya
language : en
Publisher: World Scientific
Release Date : 2018-09-28
Hybrid Metaheuristics Research And Applications written by Siddhartha Bhattacharyya and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-28 with Computers categories.
A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.Related Link(s)
Metaheuristics
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Author : El-Ghazali Talbi
language : en
Publisher: John Wiley & Sons
Release Date : 2009-05-27
Metaheuristics written by El-Ghazali Talbi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-27 with Computers categories.
A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.
Materials Informatics I
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Author : Kunal Roy
language : en
Publisher: Springer Nature
Release Date : 2025-05-10
Materials Informatics I written by Kunal Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-10 with Science categories.
This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas.
Metaheuristic Optimization Nature Inspired Algorithms Swarm And Computational Intelligence Theory And Applications
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Author : Modestus O. Okwu
language : en
Publisher: Springer Nature
Release Date : 2020-11-13
Metaheuristic Optimization Nature Inspired Algorithms Swarm And Computational Intelligence Theory And Applications written by Modestus O. Okwu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Technology & Engineering categories.
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
Machine Learning Advances In Computing Renewable Energy And Communication
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Author : Anuradha Tomar
language : en
Publisher: Springer Nature
Release Date : 2021-08-19
Machine Learning Advances In Computing Renewable Energy And Communication written by Anuradha Tomar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-19 with Technology & Engineering categories.
This book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2020), held in Krishna Engineering College, Ghaziabad, India, during December 17–18, 2020. This book discusses key concepts, challenges, and potential solutions in connection with established and emerging topics in advanced computing, renewable energy, and network communications.
Python Based Evolutionary Algorithms For Engineers
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Author : Pankaj Jayaraman
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Python Based Evolutionary Algorithms For Engineers written by Pankaj Jayaraman and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
"Python-Based Evolutionary Algorithms for Engineers" is a comprehensive guide designed to empower engineers with the knowledge and skills needed to harness the power of evolutionary algorithms in optimization tasks. We seamlessly integrate theoretical foundations with hands-on implementation, making it accessible to both beginners and seasoned practitioners. Starting with fundamental concepts, we progress to a dedicated exploration of Differential Evolution, a versatile optimization technique, with a strong emphasis on practical Python implementations. Readers will delve into the intricacies of multi-objective optimization and discover the myriad applications of evolutionary algorithms across diverse engineering domains. Our book stands out by offering a hands-on approach, allowing readers to translate theoretical concepts into practical applications using Python. We provide clear explanations and real-world examples that equip engineers to implement and adapt powerful optimization techniques. We also explore multi-objective optimization, demonstrating the versatility of evolutionary algorithms in addressing complex engineering challenges. With a strong emphasis on applicability, our book serves as a guide for both newcomers and experienced practitioners, offering a pathway to proficiently leverage evolutionary algorithms for enhanced problem-solving and innovation in engineering projects.