Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms
Editorial:
Springer
Fecha de edición:
2010
Cita:
Di Chio, Cecilia, et al. (eds.) (2010) Applications of Evolutionary Computation: EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Istanbul, Turkey, April 7-9, 2010, Proceedings, Part I. (Lecture Notes in Computer Science, 6024). Springer, pp. 512- 521
ISBN:
978-3-642-12239-2 (print)
978-3-642-12238-5 (online)
ISSN:
0302-9743
DOI:
10.1007/978-3-642-12239-2_53
Patrocinador:
This work was supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT
TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-0
Serie/Num.:
Lecture Notes in Computer Science
6024
Proyecto:
Comunidad de Madrid. S2009/TIC-1485/CONTEXTS
Palabras clave:
MOEAs
,
Model building
,
Algorithms
Derechos:
© 2010 Springer-Verlag Berlin Heidelberg
Resumen:
In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model-building algorithms. Most current model-building schemes used so far off-the-shelf machine learning methods. These methods are mostly error-based learn
In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model-building algorithms. Most current model-building schemes used so far off-the-shelf machine learning methods. These methods are mostly error-based learning algorithms. However, the model-building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertions
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Nota:
Proceedings of: 3rd European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM 2010) [associated to: EvoApplications 2010. European Conference on the Applications of Evolutionary Computation]. Istambul, Turkey, April 7-9, 2010
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