  Widely used in hydraulics, the Colebrook equation for flow friction relates
implicitly to the input parameters; the Reynolds number, and the relative
roughness of inner pipe surface, with the output unknown parameter; the flow
friction factor. In this paper, a few explicit approximations to the Colebrook
equation are generated using the ability of artificial intelligence to make
inner patterns to connect input and output parameters in explicit way not
knowing their nature or the physical law that connects them, but only knowing
raw numbers. The fact that the used genetic programming tool does not know the
structure of the Colebrook equation which is based on computationally expensive
logarithmic law, is used to obtain better structure of the approximations which
is less demanding for calculation but also enough accurate. All generated
approximations are with low computational cost because they contain a limited
number of logarithmic forms used although for normalization of input parameters
or for acceleration, but they are also sufficiently accurate. The relative
error regarding the friction factor in best case is up to 0.13% with only two
logarithmic forms used. As the second logarithm can be accurately approximated
by the Pade approximation, practically the same error is obtained also using
only one logarithm.
