New applications of Levin's Universal Optimal Search are presented in this paper. Using this method one finds solutions to a few chosen problems. Such solutions are characterized by possibility of the maximal generalization. In the deterministic version of the Universal Optimal Search algorithm one can always generate the best solution.

It is shown that it is possible to find a solution by this method for problems in neural networks for which back-propagation type of methods face difficulties.

Another application considered here is a problem of finding an exit-way from a maze.

There exists a probabilistic version of Levin's universal search. This work offers an alternative implementation of the Levin's Universal Optimal Search.

Projects on similar subject and BACK to the on-line publications of W. Duch.