  A procedure for generating non-differentiable, continuously differentiable,
and twice continuously differentiable classes of test functions for
multiextremal multidimensional box-constrained global optimization and a
corresponding package of C subroutines are presented. Each test class consists
of 100 functions. Test functions are generated by defining a convex quadratic
function systematically distorted by polynomials in order to introduce local
minima. To determine a class, the user defines the following parameters: (i)
problem dimension, (ii) number of local minima, (iii) value of the global
minimum, (iv) radius of the attraction region of the global minimizer, (v)
distance from the global minimizer to the vertex of the quadratic function.
Then, all other necessary parameters are generated randomly for all 100
functions of the class. Full information about each test function including
locations and values of all local minima is supplied to the user. Partial
derivatives are also generated where possible.
