Adaptive differential evolution : a robust approach to multimodal problem optimization

Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics su...

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Príomhchruthaitheoirí: Zhang, Jingqiao, Sanderson, Arthur C. (Údar)
Formáid: Livre numérique
Teanga:Anglais
Foilsithe / Cruthaithe: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Eagrán:1st ed. 2009.
Sraith:Evolutionary Learning and Optimization 1
Rochtain ar líne:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Nóta: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Adaptive Differential Evolution, Texte imprimé, 9783642015267
• Adaptive Differential Evolution, Texte imprimé, 9783642015281
• Adaptive Differential Evolution, Texte imprimé, 9783642015267
• Adaptive Differential Evolution, Texte imprimé, 9783642260216
• Adaptive Differential Evolution, Texte imprimé, 9783642015281
• Adaptive Differential Evolution, Texte imprimé, 9783642015267
• Adaptive Differential Evolution, Texte imprimé, 9783642260216
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245 1 0 |a Adaptive differential evolution :  |b a robust approach to multimodal problem optimization   |c by Jingqiao Zhang, Arthur C. Sanderson. 
250 |a 1st ed. 2009. 
260 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg. 
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505 1 |a Related Work and Background Theoretical Analysis of Differential Evolution Parameter Adaptive Differential Evolution Surrogate Model-Based Differential Evolution Adaptive Multi-objective Differential Evolution Application to Winner Determination Problems in Combinatorial Auctions Application to Flight Planning in Air Traffic Control Systems Application to the TPM Optimization in Credit Decision Making Conclusions and Future Work 
506 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
506 |a Accès soumis à abonnement pour tout autre établissement 
506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions. Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include: Theoretical analysis of differential evolution and its control parameters Algorithmic design and comparative analysis of parameter adaptive schemes Scalability analysis of adaptive differential evolution Adaptive differential evolution for multi-objective optimization Incorporation of surrogate model for computationally expensive optimization Application to winner determination in combinatorial auctions of E-Commerce Application to flight route planning in Air Traffic Management Application to transition probability matrix optimization in credit-decision making 
700 1 |a Sanderson, Arthur C.  |4 aut 
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