  The evaluation of the minimum distance of linear block codes remains an open
problem in coding theory, and it is not easy to determine its true value by
classical methods, for this reason the problem has been solved in the
literature with heuristic techniques such as genetic algorithms and local
search algorithms. In this paper we propose two approaches to attack the
hardness of this problem. The first approach is based on genetic algorithms and
it yield to good results comparing to another work based also on genetic
algorithms. The second approach is based on a new randomized algorithm which we
call Multiple Impulse Method MIM, where the principle is to search codewords
locally around the all-zero codeword perturbed by a minimum level of noise,
anticipating that the resultant nearest nonzero codewords will most likely
contain the minimum Hamming-weight codeword whose Hamming weight is equal to
the minimum distance of the linear code.
