Informatica

Results of complexity theory show the limitations of exact analysis. That explains popularity of heuristic algorithms. It is well known that efficiency of heuristics depends on the parameters. Therefore automatic procedures for tuning the heuristics help to compare results of different heuristics and enhance their efficiency.

The theory and some applications of Bayesian Approach were discussed in (Mockus, 2006a). In this paper examples of Bayesian Approach to automated tuning of heuristics are investigated. This is the Bayesian Heuristic Approach, in short. The examples of traditional methods of optimization, including applications of linear and dynamic programming, will be investigated in the next paper. These three papers represents three parts of the same work. However each part can be read independently.

All the algorithms are implemented as platform independent Java applets or servlets. Readers can easily verify and apply the results for studies and for real life optimization problems.

The theoretical result is application of unified Bayesian Heuristic Approach for different discrete optimization models. The practical result is adaptation of these models for graduate distance studies and scientific collaboration by a common java global optimization framework.

The software is regularly updated and corrected responding to new programming tools and users reports. However the general structure of web sites remains. The information is on the web site: http://pilis.if.ktu.lt/~mockus and four mirror sites.