Algorithms With Greedy Heuristic Procedures for Mixture Probability Distribution Separation

Lev Kazakovtsev, Dmitry Stashkov, Mikhail Gudyma, Vladimir Kazakovtsev

For clustering problems based on the model of mixture probability distribution separation, we propose new Variable Neighborhood Search algorithms (VNS) and evolutionary genetic algorithms (GA) with greedy agglomerative heuristic procedures and compare them with known algorithms. New genetic algorithms implement a global search strategy with the use of a special crossover operator based on greedy agglomerative heuristic procedures in combination with the EM algorithm (Expectation Maximization). In our new VNS algorithms, this combination is used for forming randomized neighborhoods to search for better solutions. The results of computational experiments made on classical data sets and the testings of production batches of semiconductor devices shipped for the space industry demonstrate that new algorithms allow us to obtain better results, higher values of the log likelihood objective function, in comparison with the EM algorithm and its modifications.