A Markov Network Based Factorized Distribution Algorithm for Optimization

Roberto Santana

Abstract: In this paper we propose a population based optimization method that uses the estimation of probability distributions. To represent an approximate factorization of the probability, the algorithm employs a junction graph constructed from an independence graph. We show that the algorithm extends the representation capabilities of previous algorithms that use factorizations. A number of functions are used to evaluate the performance of our proposal. The results of the experiments show that the algorithm is able to optimize the functions, outperforming other evolutionary algorithms that use factorizations.