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Trade paperback. Zustand: Very good. ix, [3], 304, [4] pages. Illustrations. Timeline. List of Abbreviations. Author's Note. Notes. Index. Signed and dated by the author on the title page. Cover has slight wear. Xu Fangfang is artist Xu Beihong's daughter. Born and raised in Beijing, China, Xu Fangfang graduated in piano performance from Beijing's Preparatory Music High School affiliated to the Central Conservatory of Music. She moved to the U.S. in 1981 and earned a B.A. in history from the University of California, Berkeley, and an M.B.A. from Stanford University. In 2000, she became the founding director of the music department at Renmin University of China. She helped initiate and facilitate the first comprehensive solo U.S. exhibition of her father Xu Beihong's work, presented by the Denver Art Museum, Oct. 2011-Jan. 2012. She has published several articles on Xu Beihong and his art, including "Xu Beihong, Pioneer of Modern Chinese Painting" in Arts of Asia 42, no. 1 (2012), and "Xu Beihong's Life and Art" in Xu Beihong: Pioneer of Modern Chinese Painting, Denver Art Museum, 2011. Famous for his ink brush paintings of galloping horses, pioneering Chinese artist Xu Beihong (1895-1953) integrated Chinese and Western painting and mentored generations of Chinese artists. Galloping Horses describes how his family and legacy survived the turbulence of Mao's ever-changing policies, which dictated the direction of art and music from 1949 through the devastating ten-year Cultural Revolution. Xu Beihong's daughter narrates this story of the Xu family, its tragedies, and the perseverance of Xu's widow Liao Jingwen, keeper of her husband's legacy for sixty years after his death. The author, a pianist and arts administrator, together with Xu Beihong's son, an artist and art teacher, and their mother, with commitment and struggle, carried forward Xu's legacy and accomplished his unfulfilled missions. The author describes her life and education as a music student in Mao Zedong's China and her work as a professional musician in Jiang Qing's model Peking opera. Xu Beihong (19 July 1895 - 26 September 1953), also known as Ju Péon, was a Chinese artist. He was primarily known for his Chinese ink paintings of horses and birds and was one of the first Chinese artists to articulate the need for artistic expressions that reflected a modern China at the beginning of the 20th century. He was also regarded as one of the first to create monumental oil paintings with epic Chinese themes - a show of his high proficiency in an essential Western art technique. He was one of the four pioneers of Chinese modern art who earned the title of "The Four Great Academy Presidents". Beginning in 1919, Xu studied overseas in Paris at the École Nationale Supérieure des Beaux-Arts, where he studied oil painting and drawing. He was known as Ju Peon while studying in Paris. His painting has been showcased in various platforms and used by learner in understanding what transpired during the early period in relation to Chinese art industry. His travels around Western Europe allowed him to observe and learn Western art techniques. Xu Beihong still wrote regularly for the Daily University of Peking University that dealt with wider issues then just campus politics. Xu addressed issues of Art and Art History and in 1920 a university art journal called Painting Miscellany was published. He and his partner Jiang Biwei came back to China in 1927 and, from 1927 to 1929, he gained a number of posts at institutions in China, including teaching at National Central University (now Nanjing University) in the former capital city Nanjing. In 1933, Xu organized an exhibition of modern Chinese painting that traveled to France, Germany, Belgium, Italy, and the Soviet Union. After the founding of the People's Republic of China in 1949, Xu became president of the Central Academy of Fine Arts and chairman of the China Artists Association. Xu developed new visual art techniques and international aesthetics, in a bid to reinvent Chinese art. First Edition [stated], presumed first printing.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 372 | Sprache: Englisch | Produktart: Bücher | This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP¿s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

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Taschenbuch. Zustand: Neu. Neuware.

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Taschenbuch. Zustand: Neu. Genetic Programming for Production Scheduling | An Evolutionary Learning Approach | Fangfang Zhang (u. a.) | Taschenbuch | Machine Learning: Foundations, Methodologies, and Applications | xxxiii | Englisch | 2022 | Springer | EAN 9789811648618 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Hardcover. Zustand: Brand New. 490 pages. 9.45x6.69x1.42 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future.Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

Hardcover. Zustand: Brand New. 688 pages. 6.13x1.66x9.25 inches. In Stock.

Paperback. Zustand: Brand New. 369 pages. 9.25x6.10x0.91 inches. In Stock.

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