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Evolutionary computation techniquesa...

Cuevas, Erik.

Evolutionary computation techniquesa comparative perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary computation techniquesby Erik Cuevas, Valentin Osuna, Diego Oliva.
Reminder of title:	a comparative perspective /
Author:	Cuevas, Erik.
other author:	Osuna, Valentin.
Published:	Cham :Springer International Publishing :2017.
Description:	xv, 222 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Evolutionary computation.
Online resource:	http://dx.doi.org/10.1007/978-3-319-51109-2
ISBN:	9783319511092$q(electronic bk.)

Evolutionary computation techniquesa comparative perspective /
Cuevas, Erik.

Evolutionary computation techniquesa comparative perspective /[electronic resource] :by Erik Cuevas, Valentin Osuna, Diego Oliva. - Cham :Springer International Publishing :2017. - xv, 222 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6861860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783319511092$q(electronic bk.)

Standard No.: 10.1007/978-3-319-51109-2doiSubjects--Topical Terms:

231709
Evolutionary computation.

LC Class. No.: TA347.E96

Dewey Class. No.: 006.3823

Evolutionary computation techniquesa comparative perspective /
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245 1 0 $a Evolutionary computation techniques $h [electronic resource] : $b a comparative perspective / $c by Erik Cuevas, Valentin Osuna, Diego Oliva.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xv, 222 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.686
505 0 $a 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.
520 $a 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.
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650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
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