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\title{The Frequency Modulation Sounds Parameter Identification Problem}
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The Parameter Identification Problem of Frequency Modulation Sounds (FMS) \cite{TF93,TF97} consist in determining the 6
parameters $a_1$, $\omega_1$, $a_2$, $\omega_2$, $a_3$, $\omega_3$  of FM Sound model represented by

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$y(t) = a_1  \cdot  \sin ( \omega_1 \cdot t \cdot \theta + a_2 \cdot \sin(\omega_2 \cdot t \cdot \theta + a_3 \cdot
\sin(\omega_3 \cdot t \cdot \theta)))$
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\noindent with $\theta = (2  \cdot  \pi / 100)$. The fitness function is defined as the summation of square errors
between the evolved data and the model data, as follows:

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$f_{fms}(a_1,\omega_1,a_2,\omega_2,a_3,\omega_3) = \sum_{t=0}^{100} (y(t) - y_0(t))^2$
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\noindent The model data are given by the following equation:

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$y_0(t) = 1.0 \cdot \sin(5.0 \cdot t \cdot \theta - 1.5 \cdot \sin(4.8 \cdot t \cdot \theta + 2.0  \cdot  \sin(4.9
\cdot t \cdot \theta)))$.
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Each parameter in the range -6.4 to 6.35. This is a highly complex multimodal problem having strong epistasis.


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