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**Darwinian networks**
(DNs)
were proposed to simplify working with
Bayesian network
(BNs).
Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model.
The graphical manipulation of the tables then takes on a biological feel, where a conditional probability table (CPT) $P(X|Y)$ is viewed as the novel representation of a *population* $p(C,D)$ using both *combative* traits $C$ (coloured clear) and *docile* traits $D$ (coloured dark).

DNs can unify modeling and reasoning tasks into a single platform.
DNs can represent exact inference using either
variable elimination or
arc-reversal,
lazy propagation,
as well as how DNs can represent testing independencies.
**Evolution** is used to represent inference.
The query $P(X|Y)$ posed to a BN ${\cal B}$ is represented by DN ${\cal D}^{\prime} = \{ p(X,Y) \}$.
**Adaptation** is used to represent the testing of an independence $I_{\cal{B}}(X,Y,Z)$ holding in a BN $\cal{B}$.
The test of independence $I_{\cal{B}}(X,Y,Z)$ holds in a BN $\cal{B}$ if and only if the adaptation $A({\cal{P}}_X, {\cal{P}}_Y, {\cal{P}}_Z)$ succeeds in the DN $\cal{D}$ for $\cal{B}$.