Zhi-Hua Zhou; Yang Yu; Chao Qian Evolutionary Learning: Advances in Theories and Algorithms 9789811359552

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Автор: Zhi-Hua Zhou; Yang Yu; Chao Qian
Название: Evolutionary Learning: Advances in Theories and Algorithms
ISBN: 9789811359552
Издательство: Springer
Классификация:

Алгоритмы и структуры данных

Математическая теория вычислений

Искусственный интеллект

ISBN-10: 9811359555
Обложка/Формат: Hardcover
Страницы: 361
Вес: 0.71 кг.
Дата издания: 2019
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 20 illustrations, color; 39 illustrations, black and white; xii, 361 p. 59 illus., 20 illus. in color.
Размер: 164 x 242 x 27
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.
Дополнительное описание: 1.Introduction.- 2. Preliminaries.- 3. Running Time Analysis: Convergence-based Analysis.- 4. Running Time Analysis: Switch Analysis.- 5. Running Time Analysis: Comparison and Unification.- 6. Approximation Analysis: SEIP.- 7. Boundary Problems of EAs.- 8

Evolutionary Learning: Advances in Theories and Algorithms, Zhi-Hua Zhou; Yang Yu; Chao Qian