Variants of Evolutionary Algorithms for Real-World Applications

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Chiong, Raymond (Editor ), Weise, Thomas (Editor ), Michalewicz, Zbigniew (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Edición:1st ed. 2012.
Temas:
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Variants of Evolutionary Algorithms for Real-World Applications  |h [electronic resource] /  |c edited by Raymond Chiong, Thomas Weise, Zbigniew Michalewicz. 
250 |a 1st ed. 2012. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2012. 
300 |a XIV, 466 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Section I: Introduction -- Section II: Planning & Scheduling -- Section III: Engineering -- Section IV: Data Collection, Retrieval & Mining. 
520 |a Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book "Variants of Evolutionary Algorithms for Real-World Applications" aims to promote the practitioner's view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation. 
650 0 |a Computational intelligence. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence. 
700 1 |a Chiong, Raymond.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Weise, Thomas.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Michalewicz, Zbigniew.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783642440588 
776 0 8 |i Printed edition:  |z 9783642234255 
776 0 8 |i Printed edition:  |z 9783642234231 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-642-23424-8  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

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