Abstract
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/sup 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 R/sup n/. 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/sup P/ norms (1<p</spl infin/).
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