diff --git a/edo/src/edoNormalAdaptive.h b/edo/src/edoNormalAdaptive.h
new file mode 100644
index 00000000..499af91f
--- /dev/null
+++ b/edo/src/edoNormalAdaptive.h
@@ -0,0 +1,91 @@
+
+/*
+The Evolving Distribution Objects framework (EDO) is a template-based,
+ANSI-C++ evolutionary computation library which helps you to write your
+own estimation of distribution algorithms.
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+
+Copyright (C) 2010 Thales group
+*/
+/*
+Authors:
+    Johann Dreo <johann.dreo@thalesgroup.com>
+    Pierre Savéant <pierre.saveant@thalesgroup.com>
+*/
+
+#ifndef _edoNormalAdaptive_h
+#define _edoNormalAdaptive_h
+
+#include "edoDistrib.h"
+
+#ifdef WITH_EIGEN
+
+#include <Eigen/Dense>
+
+template < typename EOT >
+class edoNormalAdaptive : public edoDistrib< EOT >
+{
+public:
+    typedef typename EOT::AtomType AtomType;
+    typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
+    typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
+
+    edoNormalAdaptive( unsigned int dim ) :
+        _mean( Vector::Zero(dim) ),
+        _C( Matrix::Identity(dim,dim) ),
+        _B( Matrix::Identity(dim,dim) ),
+        _D( Vector::Constant( dim, 1) ),
+        _sigma(1.0),
+        _p_c( Vector::Zero(dim) ),
+        _p_s( Vector::Zero(dim) )
+    {
+        assert( dim > 0);
+    }
+
+    unsigned int size()
+    {
+        return _mean.innerSize();
+    }
+
+    Vector mean() const {return _mean;}
+    Matrix covar() const {return _covar;}
+    Matrix coord_sys() const {return _B;}
+    Vector scaling() const {return _D;}
+    double sigma() const {return _sigma;}
+    Vector path_covar() const {return _p_c;}
+    Vector path_sigma() const {return _p_s;}
+
+    void mean(       Vector m ) { _mean = m; }
+    void covar(      Matrix c ) { _C = c; }
+    void coord_sys(  Matrix b ) { _B = b; }
+    void scaling(    Vector d ) { _D = d; }
+    void sigma(      double s ) { _sigma = s; }
+    void path_covar( Vector p ) { _path_covar = p; }
+    void path_sigma( Vector p ) { _path_sigma = p; }
+
+private:
+    Vector _mean; // 
+    Matrix _C; // covariance matrix
+    Matrix _B; // eigen vectors / coordinates system
+    Vector _D; // eigen values / scaling
+    double _sigma; // 
+    Vector _p_c; // evolution path for C
+    Vector _p_s; // evolution path for sigma
+};
+
+#endif // WITH_EIGEN
+
+#endif // !_edoNormalAdaptive_h