Metaheuristic And Machine Learning Optimization Strategies For Complex
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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.
Metaheuristic And Machine Learning Optimization Strategies For Complex
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Author :
language : en
Publisher:
Release Date : 2024
Metaheuristic And Machine Learning Optimization Strategies For Complex written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Metaheuristic And Machine Learning Optimization Strategies For Complex Systems
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Author : Suchithra M.
language : en
Publisher:
Release Date : 2024
Metaheuristic And Machine Learning Optimization Strategies For Complex Systems written by Suchithra M. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Metaheuristics For Machine Learning
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Author : Kanak Kalita
language : en
Publisher: John Wiley & Sons
Release Date : 2024-03-28
Metaheuristics For Machine Learning written by Kanak Kalita 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 2024-03-28 with Computers categories.
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.
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.
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.
Optimization In Machine Learning And Applications
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Author : Anand J. Kulkarni
language : en
Publisher: Springer
Release Date : 2020-12-10
Optimization In Machine Learning And Applications written by Anand J. Kulkarni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-10 with Technology & Engineering categories.
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Metaheuristics In Water Geotechnical And Transport Engineering
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Author : Xin-She Yang
language : en
Publisher: Newnes
Release Date : 2012-09
Metaheuristics In Water Geotechnical And Transport Engineering written by Xin-She Yang and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09 with Computers categories.
Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence. Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation Develops new hybrid and advanced methods suitable for civil engineering problems at all levels Appropriate for researchers and advanced students to help to develop their work
Essentials Of Metaheuristics
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Author : Sean Luke
language : en
Publisher:
Release Date : 2009
Essentials Of Metaheuristics written by Sean Luke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Algorithms categories.
Machine Learning And Metaheuristic Computation
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Author : Erik Cuevas
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-05
Machine Learning And Metaheuristic Computation written by Erik Cuevas 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 2024-11-05 with Computers categories.
Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence, machine learning and metaheuristic computation, have an enormous range of individual and combined applications in computer science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. Machine Learning and Metaheuristic Computation offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge artificial intelligence tools. The text also provides: Treatment suitable for readers with only basic mathematical training Detailed discussion of topics including dimensionality reduction, clustering methods, differential evolution, and more A rigorous but accessible vision of machine learning algorithms and the most popular approaches of metaheuristic optimization Machine Learning and Metaheuristic Computation is ideal for students, researchers, and professionals looking to combine these vital methods to solve problems in optimization approaches.