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.

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. Thus we need some automatic procedures for tuning the heuristics. That helps comparing results of different heuristics. This enhance their efficiency, too.

An initial presentation of the basic ideas is in (Mockus, 2000). Preliminary results of distance graduate studies are in (Mockus, 2006a). Examples of optimization of sequential statistical decisions are in (Mockus, 2006b).

In this paper the theory and applications of Bayesian Heuristic Approach are discussed. In the next paper examples of Bayesian Approach to automated tuning of heuristics will be regarded. The examples of traditional methods of optimization including applications of linear and dynamic programming will be investigated in the last paper. These 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 models.

The information is on the main web-site http://pilis.if.ktu.lt/∼jmockus and four mirrors.

In many cases the average deviation is a stochastic and multi-modal function of heuristic parameters. Thus a stochastic global optimization is needed. The Bayesian heuristic approach is developed and applied for this optimization. That is main distinctive feature of this work. The approach is illustrated by flow-shop and school scheduling examples. Two versions of school scheduling models are developed for both traditional and profiled schools. The models are tested while designing schedules for some Lithuanian schools. Quality of traditional schedules is defined by the number of teacher “windows”. Schedules of profiled schools are evaluated by user defined penalty functions. That separates clearly subjective and objective data. This is the second specific feature of the proposed approach.

The software is developed for the Internet environment and is used as a tool for research collaboration and distance graduate studies. The software is available at web-sites and can be ran by standard net browsers supporting Java language. The care is taken that interested persons could easily test the results and apply the algorithms and software for their own problems.