Название: Intelligent Optimization: Principles, Algorithms and ApplicationsАвтор: Changhe Li, Shoufei Han, Sanyou Zeng
Издательство: Springer
Год: 2024
Страниц: 369
Язык: английский
Формат: pdf (true), epub
Размер: 34.6 MB
This textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-objective optimization, dynamic optimization, constrained optimization, robust optimization, expensive optimization, and other pivotal scientific studies related to optimization.
Firstly, the relationship between optimization and Machine Learning is discussed, and an example of Machine Learning task is given. Secondly, the mathematical formulation of an optimization problem is defined. Optimization problems are categorized into continuous optimization problems and discrete optimization problems. Finally, optimization algorithms are introduced and categorized into deterministic algorithms and probabilistic algorithms, where several terms regarding intelligent optimization are introduced. AI is an interdisciplinary science with multiple approaches in mathematics, Computer Science, biology, neuroscience, psychology, sociology, linguistics, philosophy, and more. Recent advances in Machine Learning, especially Deep Learning, has been successfully applied in games, transportation, medical treatment, industry, etc.
Generally speaking, the target of application of a Machine Learning method is to create a model to reflect the relationship between input features (weather for tomorrow, all the pixels of your photo) and desired output (the rate of students being late for the first class, who you are); then we can use this model to predict the result according to new input features. However, have you ever thought about how Machine Learning methods can do such things? To answer this question, we have to get deeper into the inner world of Machine Learning. Do not worry, we just need to take maybe the simplest Machine Learning problem, a linear regression problem, for example.
Designed to be approachable and inclusive, this textbook equips readers with the essential mathematical background necessary for understanding intelligent optimization. It employs an accessible writing style, complemented by extensive pseudo-code and diagrams that vividly illustrate the mechanisms, principles, and algorithms of optimization. With a focus on practicality, this textbook provides diverse real-world application examples spanning engineering, games, logistics, and other domains, enabling readers to confidently apply intelligent techniques to actual optimization problems.
Recognizing the importance of hands-on experience, the textbook introduces the Open-source Framework for Evolutionary Computation platform (OFEC) as a user-friendly tool. This platform serves as a comprehensive toolkit for implementing, evaluating, visualizing, and benchmarking various optimization algorithms. The book guides readers on maximizing the utility of OFEC for conducting experiments and analyses in the field of evolutionary computation, facilitating a deeper understanding of intelligent optimization through practical application.
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