On the convergence of a class of estimation of distribution algorithms

  • Authors:
  • Qingfu Zhang;H. Muhlenbein

  • Affiliations:
  • Dept. of Comput. Sci., Essex Univ., Colchester, UK;-

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2004

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Abstract

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.