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Self-organizing migrating algorithm ...

Davendra, Donald.

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Self-organizing migrating algorithm = methodology and implementation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Self-organizing migrating algorithm/ edited by Donald Davendra, Ivan Zelinka.
其他題名:	methodology and implementation /
其他作者:	Davendra, Donald.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xviii, 289 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Evolutionary computation. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-28161-2
ISBN:	9783319281612$q(electronic bk.)

Self-organizing migrating algorithm = methodology and implementation /
Self-organizing migrating algorithmmethodology and implementation /[electronic resource] :edited by Donald Davendra, Ivan Zelinka. - Cham :Springer International Publishing :2016. - xviii, 289 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6261860-949X ;. - Studies in computational intelligence ;v.379..

This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

ISBN: 9783319281612$q(electronic bk.)

Standard No.: 10.1007/978-3-319-28161-2doiSubjects--Topical Terms:

582189
Evolutionary computation.

LC Class. No.: QA76.618

Dewey Class. No.: 006.3823

Self-organizing migrating algorithm = methodology and implementation /
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520 $a This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
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