دانلود کتاب یادگیری ماشین پیشرفته با تکنیک های تکاملی و فراابتکاری

In this edited book Advanced Machine Learning with Evolutionary and Metaheuristic Techniques, we explore the intricate synergy between the domains of optimization and machine learning, with a focus on the cutting-edge techniques rooted in evolutionary and metaheuristic approaches. In today’s rapidly evolving technological landscape, the realms of machine learning and evolutionary optimiza-tion techniques have forged powerful alliances that are useful for solving real-life problems.
The opening chapters provide a thorough understanding of Evolutionary Algo-rithms (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.
The following is a synopsis of individual chapter’s content that might be more helpful to readers.
Chapter “From Evolution to Intelligence: Exploring the Synergy of Optimization and Machine Learning” delves into the fundamental principles of machine learning and evolutionary optimization. This chapter further helps the reader discover how machine learning algorithms harness the power of data to construct predictive and descriptive models, while evolutionary optimization algorithms mimic the process of evolution to iteratively refine potential solutions, ultimately seeking optimal or near-optimal outcomes.
Chapter “Metaheuristic and Evolutionary Algorithms in Explainable Artificial Intelligence” explores the realm of Explainable Artificial Intelligence (XAI). The transparency and interpretability of machine learning models have become critical concerns in high-stakes decision-making scenarios. This chapter reveals how the combination of evolutionary and metaheuristic techniques offer promising solutions to make AI models more understandable and transparent.
Chapter “Evolutionary Dynamic Optimization and Machine Learning” dives into the world of Evolutionary Dynamic Optimization (EDO) and its reciprocal integra-tion with Machine Learning. EDO holds the key to addressing complex, dynamic objective functions and provides valuable insights for enhancing machine learning processes.
Chapter “Evolutionary Techniques in Making Efficient Deep-Learning Framework: A Review” brings a fresh perspective to deep learning. By incorporat-ing evolutionary algorithms and metaheuristics, it discusses optimization of deep learning models, fine-tuning model architecture and hyperparameters to improve performance and efficiency.
Chapter “Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization” illustrates the integration of Particle Swarm Optimization (PSO) with Reinforcement Learning (RL), offering a promising approach to optimization. This unique combination provides solutions to complex optimization problems and sets the stage for further advancements in both fields.
Chapter “Synergies Between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview” uncovers the synergies between Natural Language Processing (NLP) and Swarm Intelligence Optimization (SI). Readers can further discover the latest developments in NLP models and SI algorithms, which are revolutionizing how machines comprehend and generate human languages.
In chapter “Heuristics-Based Hyperparameter Tuning for Transfer Learning Algorithms”, the utilization of PSO is employed for the purpose of fine-tuning hyperparameters within the transfer learning framework.
Chapter “Machine Learning Applications of Evolutionary and Metaheuristic Algorithms” provides a brief introduction to applications of evolutionary and metaheuristic algorithms to real-world applications, unveiling the symbiotic rela-tionship between population-based metaheuristic algorithms and machine learning. Through examples and case studies, the reader will gain insight into how these algorithms solve complex engineering problems and drive advancements in various domains, from classification to clustering and beyond.