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Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems
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Book Synopsis Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems by : Essam Halim Houssein
Download or read book Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems written by Essam Halim Houssein and published by Springer Nature. This book was released on 2022-06-04 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. 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 can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
Book Synopsis Metaheuristics for Machine Learning by : Kanak Kalita
Download or read book Metaheuristics for Machine Learning written by Kanak Kalita and published by John Wiley & Sons. This book was released on 2024-03-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
Book Synopsis Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems by : Shubham Mahajan
Download or read book Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems written by Shubham Mahajan and published by John Wiley & Sons. This book was released on 2024-08-01 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy. Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.
Book Synopsis Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems by : Shubham Mahajan
Download or read book Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems written by Shubham Mahajan and published by John Wiley & Sons. This book was released on 2024-08-27 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy. Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.
Book Synopsis Metaheuristics for Machine Learning by : Mansour Eddaly
Download or read book Metaheuristics for Machine Learning written by Mansour Eddaly and published by Springer Nature. This book was released on 2023-03-13 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
Book Synopsis Machine Learning and Metaheuristics: Methods and Analysis by : Uma N. Dulhare
Download or read book Machine Learning and Metaheuristics: Methods and Analysis written by Uma N. Dulhare and published by Springer Nature. This book was released on 2023-12-03 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.
Book Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin
Download or read book Data-Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Book Synopsis Comprehensive Metaheuristics by : Seyedali Mirjalili
Download or read book Comprehensive Metaheuristics written by Seyedali Mirjalili and published by Elsevier. This book was released on 2023-01-31 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains. The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts. Presented by world-renowned researchers and practitioners in metaheuristics Includes techniques, algorithms, and applications based on real-world case studies Presents the methodology for formulating optimization problems for metaheuristics Provides readers with methods for analyzing and tuning the performance of a metaheuristic, as well as for integrating metaheuristics in other AI techniques Features online complementary source code from the applications and algorithms
Book Synopsis Metaheuristic Optimization Algorithms by : Laith Abualigah
Download or read book Metaheuristic Optimization Algorithms written by Laith Abualigah and published by Elsevier. This book was released on 2024-05-05 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions. World-renowned researchers and practitioners in Metaheuristics present the procedures and pseudocode for creating a wide range of optimization algorithms Helps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applications Helps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems
Book Synopsis Metaheuristic and Machine Learning Optimization Strategies for Complex Systems by : R., Thanigaivelan
Download or read book Metaheuristic and Machine Learning Optimization Strategies for Complex Systems written by R., Thanigaivelan and published by IGI Global. This book was released on 2024-07-17 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance by : Vasant, Pandian M.
Download or read book Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance written by Vasant, Pandian M. and published by IGI Global. This book was released on 2012-09-30 with total page 735 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.
Book Synopsis Metaheuristics for Machine Learning by : Kanak Kalita
Download or read book Metaheuristics for Machine Learning written by Kanak Kalita and published by John Wiley & Sons. This book was released on 2024-05-07 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
Book Synopsis Classification Applications with Deep Learning and Machine Learning Technologies by : Laith Abualigah
Download or read book Classification Applications with Deep Learning and Machine Learning Technologies written by Laith Abualigah and published by Springer Nature. This book was released on 2022-11-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies’ image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.
Book Synopsis Nature-Inspired Methods for Metaheuristics Optimization by : Fouad Bennis
Download or read book Nature-Inspired Methods for Metaheuristics Optimization written by Fouad Bennis and published by Springer Nature. This book was released on 2020-01-17 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.
Book Synopsis Quantum Computing and Supply Chain Management: A New Era of Optimization by : Hassan, Ahdi
Download or read book Quantum Computing and Supply Chain Management: A New Era of Optimization written by Hassan, Ahdi and published by IGI Global. This book was released on 2024-07-23 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today's supply chains are becoming more complex and interconnected. As a result, traditional optimization engines struggle to cope with the increasing demands for real-time order fulfillment and inventory management. With the expansion and diversification of supply chain networks, these engines require additional support to handle the growing complexity effectively. This poses a significant challenge for supply chain professionals who must find efficient and cost-effective solutions to streamline their operations and promptly meet customer demands. Quantum Computing and Supply Chain Management: A New Era of Optimization offers a transformative solution to these challenges. By harnessing the power of quantum computing, this book explores how supply chain planners can overcome the limitations of traditional optimization engines. Quantum computing's ability to process vast amounts of data from IoT sensors in real time can revolutionize inventory management, resource allocation, and logistics within the supply chain. It provides a theoretical framework and practical examples to illustrate how quantum algorithms can enhance transparency, optimize dynamic inventory allocation, and improve supply chain resilience.
Book Synopsis Intelligent Human Centered Computing by : Siddhartha Bhattacharyya
Download or read book Intelligent Human Centered Computing written by Siddhartha Bhattacharyya and published by Springer Nature. This book was released on 2023-06-14 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality research papers presented at the First Doctoral Symposium on Human Centered Computing (HUMAN 2023), jointly organized by Computer Society of India, Kolkata Chapter and Techno India University, West Bengal, on February 25, 2023. This book discusses the topics of modern human centered computing and its applications. The book showcases the fusion of human sciences (social and cognitive) with computer science (human–computer interaction, signal processing, machine learning, and ubiquitous computing).
Book Synopsis Reactive Search and Intelligent Optimization by : Roberto Battiti
Download or read book Reactive Search and Intelligent Optimization written by Roberto Battiti and published by Springer Science & Business Media. This book was released on 2008-12-16 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities for the automated tuning of these parameters.
