Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient
Zhuang L, Tang K, Jin Y (2013)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2013. Yin H, Tang K, Gao Y, Klawonn F, Lee M, Weise T, Li B, Yao X (Eds); Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 366-375.
Sammelwerksbeitrag
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Zhuang, Lili;
Tang, Ke;
Jin, YaochuUniBi 
Herausgeber*in
Yin, Hujun;
Tang, Ke;
Gao, Yang;
Klawonn, Frank;
Lee, Minho;
Weise, Thomas;
Li, Bin;
Yao, Xin
Abstract / Bemerkung
Although mixed-integer evolution strategies (MIES) have been successfully applied to optimization of mixed-integer problems, they may encounter challenges when fitness evaluations are time consuming. In this paper, we propose to use a radial-basis-function network (RBFN) trained based on the rank correlation coefficient distance metric to assist MIES. For the distance metric of the RBFN, we modified a heterogeneous metric (HEOM) by multiplying the weight for each dimension. Whilst the standard RBFN aims to approximate the fitness accurately, the proposed RBFN tries to rank the individuals (according to their fitness) correctly. Kendall rank correlation Coefficient (RCC) is adopted to measure the degree of rank correlation between the fitness and each variable. The higher the rank similarity with fitness, the greater the weight one variable will be given. Experimental results show the efficacy of the MIES assisted by the RBFN trained by maximizing the RCC performs.
Erscheinungsjahr
2013
Buchtitel
Intelligent Data Engineering and Automated Learning – IDEAL 2013
Serientitel
Lecture Notes in Computer Science
Seite(n)
366-375
ISBN
978-3-642-41277-6
eISBN
978-3-642-41278-3
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2978571
Zitieren
Zhuang L, Tang K, Jin Y. Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient. In: Yin H, Tang K, Gao Y, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013: 366-375.
Zhuang, L., Tang, K., & Jin, Y. (2013). Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient. In H. Yin, K. Tang, Y. Gao, F. Klawonn, M. Lee, T. Weise, B. Li, et al. (Eds.), Lecture Notes in Computer Science. Intelligent Data Engineering and Automated Learning – IDEAL 2013 (pp. 366-375). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_45
Zhuang, Lili, Tang, Ke, and Jin, Yaochu. 2013. “Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient”. In Intelligent Data Engineering and Automated Learning – IDEAL 2013, ed. Hujun Yin, Ke Tang, Yang Gao, Frank Klawonn, Minho Lee, Thomas Weise, Bin Li, and Xin Yao, 366-375. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg.
Zhuang, L., Tang, K., and Jin, Y. (2013). “Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient” in Intelligent Data Engineering and Automated Learning – IDEAL 2013, Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., and Yao, X. eds. Lecture Notes in Computer Science (Berlin, Heidelberg: Springer Berlin Heidelberg), 366-375.
Zhuang, L., Tang, K., & Jin, Y., 2013. Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 366-375.
L. Zhuang, K. Tang, and Y. Jin, “Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient”, Intelligent Data Engineering and Automated Learning – IDEAL 2013, H. Yin, et al., eds., Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp.366-375.
Zhuang, L., Tang, K., Jin, Y.: Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., and Yao, X. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. p. 366-375. Springer Berlin Heidelberg, Berlin, Heidelberg (2013).
Zhuang, Lili, Tang, Ke, and Jin, Yaochu. “Metamodel Assisted Mixed-Integer Evolution Strategies Based on Kendall Rank Correlation Coefficient”. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Ed. Hujun Yin, Ke Tang, Yang Gao, Frank Klawonn, Minho Lee, Thomas Weise, Bin Li, and Xin Yao. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. Lecture Notes in Computer Science. 366-375.
Link(s) zu Volltext(en)
Access Level
Closed Access
Google Scholar
ISBN Suche