Evolutionary Computation Techniques: A Comparative Perspective: Studies in Computational Intelligence, cartea 686
Autor Erik Cuevas, Valentín Osuna, Diego Olivaen Limba Engleză Paperback – 7 iul 2018
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 624.25 lei 6-8 săpt. | |
Springer International Publishing – 7 iul 2018 | 624.25 lei 6-8 săpt. | |
Hardback (1) | 630.36 lei 6-8 săpt. | |
Springer International Publishing – 7 ian 2017 | 630.36 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783319845685
ISBN-10: 3319845683
Ilustrații: XV, 222 p. 74 illus., 33 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.34 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
ISBN-10: 3319845683
Ilustrații: XV, 222 p. 74 illus., 33 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.34 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Cham, Switzerland
Cuprins
Preface.- Introduction.- Multilevel segmentation in digital images.- Multi-Circle detection on images.- Template matching.- Motion estimation.- Photovoltaic cell design.- Parameter identification of induction motors.- White blood cells Detection in images.- Estimation of view transformations in images.- Filter Design.
Textul de pe ultima copertă
This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
Caracteristici
Compares various evolutionary computation (EC) techniques when they face complex optimization problems extracted from different engineering domains Poses a complex engineering optimization problem in each self-contained chapter Presents specific EC techniques as the best choice according to their search characteristics and conducts a set of experiments to compare their performance to other popular EC methods Includes supplementary material: sn.pub/extras