Abstract: This paper is an experimental study on hypegraph partitioning using the simple genetic algorithm (GA) based on the schema theorem and the advanced algorithms based on the estimation of distribution of promising solution. Primarily we have implemented a simple GA based on the GaLib library[Gal94] and some hybrid variant included a fast heuristics to speed up the convergence of the optimization process. Secondly we have implemented the Univariate Marginal Distribution algorithm (UMDA) and the Bivariate Marginal Distribution algorithm (BMDA), both have been published even recently[Pel98] and used a share version of a superior new program BOA based on the Bayesian Optimization Algorithm [Pel99]. We have also extended the BMDA algorithm to a new version with finite alphabet encoding of chromozomes and new metric that enables the m-way partitioning graphs. The aim of our paper is to test the efficiency of new approaches for discrete combinatorial problems represented by hypergraph partitioning. Key words: decomposition, hypergraph partitioning, simple and hybrid GA, estimation of distribution algorithm, Bayesian network. 1