Skip to Main Content
Genome research is one of the most interesting and important areas of the science nowadays. It is well-known that the genomes of complex organisms are highly organized. Many studies show that DNA sequence can be divided into a few segments, which have various properties of interest. Detection of this segments is extremely significant from the point of view of practical applications, as well as for understanding evolutional processes. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are divided into segments by an unknown number of change-points, with each segment supposed to have been generated by a process with different parameters. Multiple change-point models are important in many biological applications and, specifically, in analysis of biomolecular sequences. In this paper, we propose to use genetic algorithm to identify change-points. Numerical experiments illustrate the effectiveness of our approach to the problem. We obtain estimates for the positions of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and the Cross-Entropy method. We also provide examples with real data sets to illustrate the usefulness of our method.