Multimodal optimization: An effective framework for model calibration
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- 作者：Manuel Chica ; a ; manuel.chica.serrano@gmail.com" class="auth_mail" title="E-mail the corresponding author ; José ; Barranquerob ; jose.barranquero@novelti.io" class="auth_mail" title="E-mail the corresponding author ; Tomasz Kajdanowiczc ; tomasz.kajdanowicz@pwr.edu.pl" class="auth_mail" title="E-mail the corresponding author ; Sergio Damasd ; sdamas@ugr.es" class="auth_mail" title="E-mail the corresponding author ; Ó ; scar Cordó ; ne ; ocordon@decsai.ugr.es" class="auth_mail" title="E-mail the corresponding author
- 关键词：Model calibration ; Multimodal optimization ; Genetic algorithms ; Sensitivity analysis
- 刊名：Information Sciences
- 出版年：2017
- 出版时间：1 January 2017
- 年：2017
- 卷：375
- 期：Complete
- 页码：79-97
- 全文大小：4204 K
文摘
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We propose a framework for model calibration based on niching genetic algorithms.
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It consists of interactive multimodal search, visualization, and parameters analysis.
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The output of the framework is a set of alternative model calibration solutions.
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The multimodal framework is applied to two different applications.
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Experiments showed the framework benefits when interactively validating models.