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20.4 MB
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A5B7ECA6081470CE666FDCF14F5C952D46101D0E
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May 22, 2025, 10:32 a.m.
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(Last updated: May 22, 2025, 10:32 a.m.)
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| Das D. Optimization Algorithms in Machine Learning. A Meta-heuristics Persp.2025.pdf | 20.4 MB |
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| Uploaded by andryold1 | Size 20.4 MB | Health [ 38 /19 ] | Added 2025-05-22 |
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| Uploaded by andryold1 | Size 69.3 MB | Health [ 45 /36 ] | Added 2025-11-02 |
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NOTE
SOURCE: Das D. Optimization Algorithms in Machine Learning. A Meta-heuristics Persp.2025
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COVER
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MEDIAINFO
Textbook in PDF format This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry. Artificial Intelligence which mostly simulates the human intelligence through machine is one of the most trending technologies around the globe that impacts almost every domain these days. Whereas, optimization relates to the process of finding the optimum solution to a particular problem satisfying some given constraints within AI. We decided to write this book to share our understanding of optimization leveraging meta-heuristic algorithms for solving stock market prediction. Owing to its simplicity and flexibility, meta-heuristics have been proven to be effective for solving various optimization problems. To date, there are many meta-heuristics have been developed in the literature. In line with the No Free Lunch theorem which suggests that no single meta-heuristic is the best for all optimization problems, the search for better algorithms is still a worthy endeavor. Grey Wolf Optimizer (GWO) is a meta-heuristic algorithm which is appealing to researcher due to its demonstrated performance as cited in the scientific literature. Despite its merits, GWO is not without limitation. As an example, the current best optimal individual of GWO is biased toward alpha and other individuals (e.g. beta and delta) attempt to modify their positions toward this best individual in each iteration process. This update process may cause the algorithm to fall to local optima especially in the cases where there are many competing local optima. Therefore, the book attempts to explain GWO for improvement of exploration by strengthen the searching process via several random leaders in each iteration, re-generating the random leaders in each iteration and introducing archive to verify the solution with better probability to proceed further for training and re-generation. The verification of each solution individually by modified GWO, instead of considering as a final solution, facilitates the improvement of the exploration. Subsequently, the book attempts to present an ensemble model applying Modified Grey Wolf Optimizer (MGWO) and neural network for stock prediction. Widespread models like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy (ES), and Population-based incremental learning (PBIL) dealing with the specified problems are also explored and compared in this book. The book presents stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. This book is an introduction to optimization and application of meta-heuristic algorithm, and it assumes fewer prior knowledge of this field. The first goal of this book is to demonstrate what a meta-heuristic algorithm is and what its applications are. The second goal of this book is to show how to prepare and employ a meta-heuristic algorithm for a given optimization problem: how to create models, how to test them, and how to use them. The final goal of this book is to give an understanding of how to prepare and employ a the meta-heuristic algorithm for solving stock market prediction. In this regard, GWO algorithm is chosen, because it is one of the most well-regarded meta-heuristic algorithms in the literature. This algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Besides, the GWO algorithm is applicable to challenging problems in unknown search spaces which produces better result than many other meta-heuristic algorithms. Although the algorithm is free from some limitations, the algorithm can faster decide the suitable thresholds, provide good classification rate, efficiency, and accuracy. This book has 12 chapters that is easy to grasp by any reader. Preface Challenges and Opportunities in Stock Prediction Using Optimization Techniques Optimization Methods: Deterministic Versus Stochastic Heuristic and Meta-heuristic Optimization Algorithms A Brief Review of Meta-heuristics Artificial Neural Networks: Structure and Learning Grey Wolf Optimizer: Foundations and Mathematical Models A Brief Review of Grey Wolf Optimizer: Variants and Applications A Brief Survey of Neural Networks Trained by the Grey Wolf Optimizer Improving the Exploration and Exploitation of Grey Wolf Optimizer How to Predict Stock Market Using Neural Networks and Grey Wolf Optimizer An Extensive Comparison of Metaheuristics When Training Neural Networks for Stock Prediction Future Trends in Stock Prediction Using Meta-heuristics Appendix: Stock Datasets
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