First of all, there is a lack of systematic methods that improve the performance of algorithms on unseen instances by gathering the experience from the instances solved in the past. Therefore, all the information obtained during the past runs of an algorithm is neglected when a new instance is encountered. A good example is the branchand-bound-and-remember method [34, 40], where the algorithm remembers the information derived during an instance solving, but the information is forgotten as soon as the instance is solved. Second, the development of efficient heuristic rules requires a substantial amount of time devoted to its design and testing. This process is tedious and requires a skilled human professional to fine-tune the heuristic's parameters. A typical example of this feature is genetic algorithms having many parameters for selection, cross-over, mutation, and other operators. The apparent response to the above challenges is utilizing the existing data. However, the main obstacle to the successful application of machine learning to enhance algorithms for combinatorial problems remains. It can be formulated as the following fundamental question--is it possible to extract any useful information from the solved instances and use it efficiently to accelerate solving of an unseen instance?