Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems PDF full book. Access full book title Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems.

Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems
Author: Essam Halim Houssein
Publisher: Springer Nature
Total Pages: 501
Release: 2022-06-04
Genre: Technology & Engineering
ISBN: 3030990796
GET BOOK

Download Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems Book in PDF, ePub and Kindle

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.

Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems

Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems
Author: Shubham Mahajan
Publisher: John Wiley & Sons
Total Pages: 372
Release: 2024-08-27
Genre: Computers
ISBN: 1394230923
GET BOOK

Download Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems Book in PDF, ePub and Kindle

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.

Comprehensive Metaheuristics

Comprehensive Metaheuristics
Author: Seyedali Mirjalili
Publisher: Elsevier
Total Pages: 468
Release: 2023-01-31
Genre: Computers
ISBN: 0323972675
GET BOOK

Download Comprehensive Metaheuristics Book in PDF, ePub and Kindle

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

Metaheuristics for Machine Learning

Metaheuristics for Machine Learning
Author: Mansour Eddaly
Publisher: Springer Nature
Total Pages: 231
Release: 2023-03-13
Genre: Computers
ISBN: 9811938881
GET BOOK

Download Metaheuristics for Machine Learning Book in PDF, ePub and Kindle

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.

Machine Learning and Metaheuristics: Methods and Analysis

Machine Learning and Metaheuristics: Methods and Analysis
Author: Uma N. Dulhare
Publisher: Springer Nature
Total Pages: 304
Release: 2023-12-03
Genre: Technology & Engineering
ISBN: 9819966450
GET BOOK

Download Machine Learning and Metaheuristics: Methods and Analysis Book in PDF, ePub and Kindle

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.

Data-Driven Evolutionary Optimization

Data-Driven Evolutionary Optimization
Author: Yaochu Jin
Publisher: Springer Nature
Total Pages: 393
Release: 2021-06-28
Genre: Computers
ISBN: 3030746402
GET BOOK

Download Data-Driven Evolutionary Optimization Book in PDF, ePub and Kindle

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.

Metaheuristic Optimization Algorithms

Metaheuristic Optimization Algorithms
Author: Laith Abualigah
Publisher: Elsevier
Total Pages: 291
Release: 2024-05-05
Genre: Computers
ISBN: 0443139261
GET BOOK

Download Metaheuristic Optimization Algorithms Book in PDF, ePub and Kindle

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

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance
Author: Vasant, Pandian M.
Publisher: IGI Global
Total Pages: 735
Release: 2012-09-30
Genre: Computers
ISBN: 1466620870
GET BOOK

Download Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance Book in PDF, ePub and Kindle

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.

Metaheuristic and Machine Learning Optimization Strategies for Complex Systems

Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
Author: R., Thanigaivelan
Publisher: IGI Global
Total Pages: 423
Release: 2024-07-17
Genre: Computers
ISBN:
GET BOOK

Download Metaheuristic and Machine Learning Optimization Strategies for Complex Systems Book in PDF, ePub and Kindle

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.

Classification Applications with Deep Learning and Machine Learning Technologies

Classification Applications with Deep Learning and Machine Learning Technologies
Author: Laith Abualigah
Publisher: Springer Nature
Total Pages: 287
Release: 2022-11-16
Genre: Technology & Engineering
ISBN: 303117576X
GET BOOK

Download Classification Applications with Deep Learning and Machine Learning Technologies Book in PDF, ePub and Kindle

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.