“Computational Intelligence: A Methodological Introduction” is an essential textbook that offers a comprehensive exploration into the domain of computational intelligence (CI). Aimed at demystifying the principles and practices behind systems that exhibit intelligent behavior in complex settings, this book is an invaluable resource for students and researchers alike. It covers fundamental concepts, algorithms, and practical implementations, extending into contemporary topics such as deep learning, scalarization methods, large-scale optimization algorithms, and collective decision-making algorithms.
Structured to facilitate a clear understanding of CI, the book encompasses a wide array of nature-inspired methodologies. It delves into classical areas like artificial neural networks, fuzzy systems, and evolutionary algorithms while also addressing newer approaches, including ant colony optimization and probabilistic graphical models. The content is meticulously divided into sections that systematically introduce neural networks, evolutionary algorithms, fuzzy systems, and Bayes and Markov networks, ensuring a comprehensive coverage of each area.
Designed for accessibility, the textbook caters to readers with a basic understanding of mathematics and is enriched with classroom-tested examples and definitions. Additionally, it provides electronic supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools, making it a perfect blend for educational purposes and self-study.
This enhanced edition has not only been fully revised but also expanded, incorporating cutting-edge content that reflects the latest developments in the field. Through its methodical approach, the book stands as a definitive guide to computational intelligence, equipping readers with the knowledge and tools necessary to navigate and contribute to the evolving landscape of intelligent systems.
