This correlation between the initial set of weights

and the quality of the solution resembles the existing

correlation between the initial antibody repertoire

and the quality of the response of natural immune

systems, that can be seen as a complex pattern

recognition device with the main goal of protecting

our body from malefic external invaders, called

antigens. Antibodies are the primary immune

elements that bind to antigens for their posterior

destruction by other cells [9]. The number of

antibodies contained in our immune system is

known to be much inferior to the number of possible

antigens, making the diversity and individual

binding capability the most important properties to

be exhibited by the antibody repertoire. In this

paper, we present a simulated annealing approach,

called SAND (Simulated ANnealing for Diversity),

that aims at generating a dedicated set of weights

that best covers the weight space, to be searched in

order to minimize the error surface. The strategy

assumes no a priori knowledge about the problem,

except for the assumption that the error surface has

multiple local optima. In this case, a good sampling

exploration of the error surface is necessary to

improve the chance of finding a promising region to

search for the solution. The algorithm induces

diversity in a population by maximizing an energy

function that takes into account the inverse of the

affinity among the antibodies. The weights of the

neural network will be associated with antibodies in

a way to be further elucidated.