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valadi j advanced machine learning evolutionary metaheuristic tech 2024

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Textbook in PDF format 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. The opening chapters provide a thorough understanding of Evolutionary Algorithms (EA) and Machine Learning (ML), discussing the historical context and evolution of both fields to lay the groundwork for the synergistic approach explored in the following chapters. In chapters “Evolutionary Dynamic Optimization and Machine Learning”, “Evolutionary Techniques in Making Efficient Deep-Learning Framework: A Review”, “Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization” and “Synergies Between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview”, readers may learn how to combine EA with ML approaches to solve complicated issues creatively. While chapter “Heuristics-Based Hyperparameter Tuning for Transfer Learning Algorithms” covers how to use hyperparameter tuning to increase the efficiency of machine learning algorithms, chapter “Machine Learning Applications of Evolutionary and Metaheuristic Algorithms” offers a quick overview of how EA is used in machine learning applications. The latter part of the book is dedicated to real-world case studies. Natural Language Processing (NLP) constitutes a vital facet of Artificial Intelligence, focusing on the intricate interplay between human language and computing systems. This rapidly advancing field explores how machines can comprehend, interpret, and produce human language with precision and naturalness. Concurrently, Swarm Intelligence Optimization (SI) emerges as a metaheuristic approach inspired by the collaborative behaviours observed in social animals. SI leverages these principles to tackle complex optimization challenges, efficiently discovering robust solutions within limited time frames. This chapter discusses natural language processing and Swarm Intelligence Optimization and the potential applications these technologies can have in various fields, including social media analytics, recommender systems, chatbots, and virtual assistants. Additionally, the chapter presents recent developments in these areas, including deep learning-based NLP models, such as transformer-based models, and new Swarm Intelligence Optimization algorithms, such as the bat algorithm and ant colony optimization. These new methods are shown to outperform existing approaches in terms of efficiency and accuracy, and have become widely used in practice. They have enabled the development of more complex and powerful NLP models, which have been used for a variety of tasks, such as Neural Machine Translation systems and Sentiment Analysis Classification, Question Answering, Topic Modeling, Sentiment analysis and machine translation

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valadi j advanced machine learning evolutionary metaheuristic tech 2024