Ordenação por translocação de genomas sem sinal utilizando algoritmos genéticos

Abstract

Translocations are used to measure the evolutionary distance between species. From a biological point of view two types of genomes have received attention: signed and unsigned genomes. When considering signed genomes, the problem can be solved in linear time, while, in the case of unsigned genomes the problem was shown to be N P-hard.Genetic algorithms (GAs) are proposed to solve the translocation distance problem between unsigned genomes. The approach consists in using a population composed of individuals representing signed genomes obtained from a given unsigned genome provided as input. The solution of each individual is also an admissible solution to the given genome. The fitness function used, which is the distance for signed genome, is computed linearly with an algorithm proposed by Bergeron et al.The GA based on this approach has been enhanced with two optimization techniques: memetic and opposition based learning. Also, parallelizations of the GA embedded with memetic were proposed seeking to improve both running time as the accuracy of results.The quality of the results was verified using an implementation of a 1.5+ε-approximation algorithm recently proposed by Cui et al.Experiments were performed taking as input synthetic genomes and genomes generated from biological data. The GAs provided better results than the quality control algorithm. The parallelizations showed improvements both regarding runtime as well as accuracy.A statistical analysis based on the Wilcoxon test was performed to check if the improvements in the solutions provided by enhanced GAs compared to those provided by the basic GA have some significance. This analysis can identify that the GA embedded with the technical memetic provides different (better) results than GA and that the results provided by the GA embedded with opposition based learning presents no significant difference. The test was also performed to compare the solutions of the parallelizations confirming that there are improvements of the results regarding the GA embedded memetic.

A Mapping of Game Types From a Systematic Literature Review in Relation to Levels in Bloom's Revised Taxonomy

“…Os jogos podem ser utilizados com uma diversidade de finalidades quando é levado em conta o contexto de aprendizado, pois até mesmo um jogo simples tem a capacidade de proporcionar informações factuais e praticar as habilidades dos participantes, atribuindo competências e destrezas (SILVEIRA; BARONE, 1998).…”