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The general procedure of an EDA is outlined in the following:
''t'' := 0
initialize model M(0) to represent uniform distribution over admissible solutions
'''while''' (termination criteria not met) '''do'''
''P'' := generate N>0 candidate solutions by sampling M(''t'')
''F'' := evaluate all candidate solutions in ''P''
M(t + 1) := adjust_model(''P'', ''F'', M(''t''))
''t'' := ''t'' + 1
Using explicit probabilistic models in optimization allowed EDAs to feasibly solve optimization problems that were notoriously difficult for most conventional evolutionary algorithms and traditional optimization techniques, such as problems with high levels of [[epistasis]]{{Citation needed|date=September 2017}}. Nonetheless, the advantage of EDAs is also that these algorithms provide an optimization practitioner with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem.
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