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The CPC, on the other hand, quantifies the data compression in terms of entropy of the [[marginal distribution]] over all partitions, where <math>\lambda</math> is the selected population size, <math>|\tau|</math> is the number of decision variables in the linkage set <math>\tau</math> and <math>H(\tau)</math> is the [[joint entropy]] of the variables in <math>\tau</math>
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The Bayesian network structure, on the other hand, must be built iteratively (linkage-learning). It starts with a network without edges and, at each step, adds the edge which better improves some scoring metric (e.g. [[Bayesian information criterion]] (BIC) or Bayesian-Dirichlet metric with likelihood equivalence (BDe)).<ref>{{cite journal|last1=Larrañaga|first1=Pedro|last2=Karshenas|first2=Hossein|last3=Bielza|first3=Concha|last4=Santana|first4=Roberto|title=A review on probabilistic graphical models in evolutionary computation|journal=Journal of Heuristics|date=21 August 2012|volume=18|issue=5|pages=795–819|doi=10.1007/s10732-012-9208-4|s2cid=9734434 |url=http://oa.upm.es/15826/}}</ref> The scoring metric evaluates the network structure according to its accuracy in modeling the selected population. From the built network, BOA samples new promising solutions as follows: (1) it computes the ancestral ordering for each variable, each node being preceded by its parents; (2) each variable is sampled conditionally to its parents. Given such scenario, every BOA step can be defined as
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