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<math> g^*(\mathbf{x}) = H(\mathbf{x}) f(\mathbf{x};\mathbf{u})/\ell</math>.
This, however, depends on the unknown <math>\ell</math>. The CE method aims to approximate the optimal PDF by adaptively selecting members of the parametric family that are closest (in the [[Kullback–Leibler divergence|Kullback–Leibler]] sense) to the optimal PDF <math>g^*</math>. Some modifications for improving the setting of parameters, convergence, and overall the computational efficiency of the cross-entropy method when dealing with multi-objective optimization problems have been introduced and reported<ref>{{cite journal |last1=Bekker |first1=J. |last2=Aldrich |first2=C. |title=The cross-entropy method in multi-objective optimisation: An assessment |journal=European Journal of Operational Research |date=2011 |volume=211 |issue=1 |pages=112-121 |doi=10.1016/j.ejor.2010.10.028}}</ref>, <ref>{{cite journal |last1=Giagkiozis |first1=I. |last2=Purshouse |first2=R.C. |last3=Fleming |first3=P.J. |title=Generalized decomposition and cross entropy methods for many-objective optimization |journal=Information Sciences |date=2014 |volume=282 |pages=363-387 |doi=10.1016/j.ins.2014.05.045}}</ref>,<ref>{{cite journal |last1=Haber |first1=R.E. |last2=Beruvides |first2=G. |last3=Quiza |first3=R. |last4=Hernandez |first4=A. |title=A simple multi-objective optimization based on the cross-entropy method |journal=
==Generic CE algorithm==
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