This thesis presents some implementation possibilities of the LEM (Learnable Evolution Model) algorithm with the purpose of solving problems from different domains using rules or patterns that occur in the data. The testing framework in which the modular algorithm is implemented and all components used for solving particular problems have been described. The implementation of LEM for solving problems which solution is represented by an floating point array has been done in two different ways: by discovering rules using the C4.5 algorithm boosted with AdaBoost and by discovering rules through the AQ algorithm. These implementations have been tasted on the COCO test platform. For the problems which solution is represented with a vector of bits, two ways of constructing new solutions in the learning phase have been implemented: by discovering rules using the C4.5 algorithm boosted with AdaBoost and by using the CHARM frequent itemset mining algorithm. Those implementations have been tested on the problem of creating a Boolean function which has to satisfy a combination of criteria as good as possible. The LEM learning phase for problems which solution is represented with a permutation has been realized in two ways: by finding frequent groups of values using a suffix tree and by mining sequential patterns using the CM-SPADE algorithm. These two realizations have been tested on a subset of standard TSP problems from TSPLIB. For the problems which solution is represented by a tree data structure the LEM learning phase has been achieved in two ways: using the gSpan algorithm and a version of Apriori specially tuned for tree frequent pattern mining. These two methods have been tested on two different problems: the first being Santa Fe trail problem and the second a set of symbolic regression problems.