Tatiana V. Polushina and Georgy Yu. Sofronov
change-point problem, genetic algorithm, combinatorial optimization, GC ratio
Genomes of eukaryotic organisms vary in GC ratio, that is, share of DNA bases such that C or G as contrary to T or A. Statistical identification of segments that are internally homogenous with respect to GC ratio is essential for understanding of evolutionary processes and the different functional characteristics of the genome. It appears that DNA segmentation concerns one of the most important applications involving change-point detection. Problems of this type arise in various areas, such as speech and image processing, biomedical applications, econometrics, industry and seismology. In this study, we develop a hybrid genetic algorithm for detecting change-points in binary sequences. We apply our algorithm to both synthetic and real data sets, and demonstrate that it is more effective than other well-known methods such as Markov chain Monte Carlo, Cross-Entropy and Genetic algorithms.
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