Using genetic algorithms for non-negative least error minimal norm solutions

We consider non negative solutions of a system of m real linear equations, Ax=b, in n unknowns which minimize the residual error when R ^m is equipped with a strictly convex norm. Out of these solutions we seek the one which is of the least norm for a strictly convex and smooth norm on Rⁿ. A hybrid genetic numerical algorithm for accomplishing this is given. The same problem is then solved using a purely genetic algorithm approach. The algorithms are tested for the l^P norms (1<p<∞)