Abstract
In this work, an artificial neural network is used to recognize timestamps of evolution. Timestamps are associated with outliers determined during the recognition of the genome attractors of organisms. The aim of this work is to present a new method of penetrating deep into evolution using the recognized timestamps. To achieve this aim, the neural networks of different number of layers were implemented in order to check the influence of the number of layers on the visibility of the timestamps. Moreover, the teaching process was repeated 10 times for each implemented neural network. The recognition of each organism evolution was also repeated 10 times for each taught neural network to increase the reliability of the results. It is presented, among other findings, that during the recognition of the timestamps of evolution not only the number of homologous comparisons and the lengths of compared sequences are important but also the distribution of similarities between sequences. It is also presented that the recognized timestamps allow for travel between genome attractors and reconstruct the line of organism development from the most advanced to the most primitive organisms. The results were validated by determining timestamps for exemplary sets of organisms and also in relation to semihomology approach and by phylogenetic tree generation.