The paper discusses strategies for metamodels in optimization problems involving expensive high-delity analysis tools. A strategy to assess the accuracy of the metamodels and to increase their completeness and delity during the optimization process is suggested and evaluated.
Self-Learning Metamodels for Optimization
Daniele Peri
2009
Abstract
The paper discusses strategies for metamodels in optimization problems involving expensive high-delity analysis tools. A strategy to assess the accuracy of the metamodels and to increase their completeness and delity during the optimization process is suggested and evaluated.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
