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On the convergence of a class of estimation of distribution algorithms

: Zhang, Q.; Mühlenbein, H.


IEEE Transactions on Evolutionary Computation 8 (2004), Nr.2, S.127-136
ISSN: 1089-778X
Fraunhofer IAIS ()

We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements. In this paper, we prove that: 1) if the distribution of the new elements matches that of the parent set exactly, the algorithms will converge to the global optimum under three widely used selection schemes and 2) a factorized distribution algorithm converges globally under proportional selection.