The optimization layer by layer (OLL) learning algorithm is applied to prediction of assembly-wise power and burnup distribution, the critical soluble boron concentration, and the pin power peaking factor (PPPF) with core burnup in the pressurized water reactor (PWR) of the Korean Nuclear Unit (KNU) 11. It is shown that the OLL trained neural networks can predict core depletion characteristics as accurately as the high-precision modern nodal method codes and that the OLL trained neural networks can compute core depletion characteristics about 40 times faster than the modern nodal method code. The OLL networks are then utilized for determining the optimum fuel assembly (FA) loading pattern (LP) of the equilibrium cycle KNU 11 PWR core by a simulated annealing (SA) scheme. By demonstrating that the FA LP optimization by the SA scheme can be carried out within 10 to 15 min thanks to the speedy neutronics evaluation of the OLL networks, it is proposed that the OLL networks can make a satisfactory substitute for core evaluation codes based on modern nodal methods in in-core fuel management optimization computations where neutronics analysis for a large number of trial loading patterns has to be carried out.