This research investigates the extension of a GA-based software package GENOCOP to deal with heavily constrained continuous numerical optimization problems. The presence of large numbers of constraints can produce situation where there are a number of disjoint feasible regions. Such problems are not amenable to solution with classical linear and non-linear programming techniques. GENOCOP handles constraints by dynamically adjusting the domains of its genetic operators to reflect the constraints expressed as a set of inequalities. Here GENOCOP is embedded in a Cultural Algorithm to allow the collection of information concerning the locations of feasible regions. This information is fed back to GENOCOP and used to control the direction of evolution in the population of solutions. This performance of this extended version of GENOCOP is compared with the original relative to a set of optimization problems. For these problems, a significant improvement in the rate of convergence for the extended GENOCOP system over the original was observed. The results suggest the potential benefits of storing global generalizations of individual experience when solving problems with constraints.