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x_{m+1,j} & \mbox{{ otherwise,}}  \end{array}\right.  \end{eqnarray}  where $\kappa_j,\varpi_j\sim U(0,1)$, and $U_j$ and $L_j$ stand for the upper and lower bounds of decision variable $j$, respectively. WE NEED FOR DETAILS ABOUT $p_m$ HERE.  \subsection{Self-adaptive Global best Harmony Search}  \label{ss.sghs}  The SGHS algorithm~\cite{Pan_2010} is a modification of GHS that employs a new improvisation scheme and self-adaptive parameters. First of all, Equation~\ref{e.par_ghs} is rewritten as follows:  \begin{equation}  \label{e.par_sghs}  x_{m+1,j} = x_{best,j},  \end{equation}  and Equation~\ref{e.hmcr} can be replaced by:  \begin{eqnarray}  \label{e.hmcr_sghs}  x_{m+1,j} & = & \left\{ \begin{array}{ll} x_{A,j}\pm \varphi_j \varrho & \mbox{{ with probability HMCR}} \\  \theta \in \Phi_j & \mbox{{ with probability (1-HMCR).}}  \end{array}\right.  \end{eqnarray}  \section*{Acknowledgments}  The authors are grateful to FAPESP grants \#2013/20387-7 and \#2014/16250-9, as well as CNPq grants \#470571/2013-6 and \#306166/2014-3.