期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 8, 期 2, 页码 127-136出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2003.820663
关键词
convergence; estimation of distribution algorithms (EDAs); factorized distribution algorithms (FDA)
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.
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