Focusing on the intersection of artificial intelligence and swarm intelligence, this book blends theoretical insights with practical applications. It showcases empirical examples from various real-world challenges, illustrating the effectiveness of its proposed methods in fields such as swarm robotics, silicon traffic management, and image understanding. The emphasis on real-world applicability makes it a valuable resource for understanding how these technologies can be implemented successfully.
“Practical Applications of Evolutionary Computation to Financial Engineering” presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionary Computation. This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within. The book is structured so that each chapter can be read independently from the others. Chapters 1 and 2 describe evolutionary computation. The third chapter is an introduction to financial engineering problems for readers who are unfamiliar with this area. The following chapters each deal, in turn, with a different problem in the financial engineering field describing each problem in detail and focusing on solutions based on evolutionary computation. Finally, the two appendixes describe software packages that implement the solutions discussed in this book, including installation manuals and parameter explanations.
Focusing on evolutionary algorithms, this book presents a comprehensive methodology that integrates various machine learning and deep learning techniques, such as convolutional neural networks and transfer learning. It explores optimization strategies through interdisciplinary research, making it suitable for readers at all levels of expertise. The content is designed to enhance understanding and application of these advanced tools, providing both theoretical insights and practical knowledge for diverse audiences interested in machine learning and optimization methodologies.
Focusing on the integration of swarm intelligence and evolutionary computation with deep learning, this book offers both theoretical insights and practical applications. It explores innovative methods within the realm of artificial intelligence, highlighting how these concepts can enhance deep learning techniques. Readers will gain a comprehensive understanding of this emerging approach, making it a valuable resource for those interested in advanced computational strategies.
What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms, this book outlines how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. It provides a methodology for integrating GP and machine learning techniques, establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction, system identification, financial forecasting, classification, and data mining. The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification, finanical engineering, and bio-informatics, it demonstrates how the proposed methodology can be useful in practical inductive problem solving.
