Esta investigación tuvo como objetivo encontrar la ruta más corta de n puntos en el espacio, sin corte o intersección entre las líneas generadas por los caminos. Para lograr esto, se usó el algoritmo genético, donde se aprovechó la ventaja de no competir “todos contra todos”, sino que a partir de una pequeña población puede encontrarse la posible mejor ruta en el espacio, también llamada “búsqueda local”. Para realizar el proceso evolutivo, se consideró el método de la ruleta, cruce por intercambio de 2 puntos, mutación por intercambio y método de parada a la varianza a 25 generaciones. Implementado en Matlab 8.3, se obtuvo como resultado una duración de 24.7 s y 210.6 s, con funciones de adaptación de 0.79 y 0.76, 76 y 206 generaciones a las pruebas realizadas de 10 y 100 puntos, respectivamente. Demostrando que con el algoritmo genético se encontró la posible mejor ruta corta de n puntos en el espacio y que el método de parada ideal para este problema en particular es la varianza, aunque consuma más tiempo frente cantidad de generaciones establecidas.

This research aims to cover a need to predict the behavior of the number of deaths in a country that has ended quarantine due to Covid-19.To achieve this, a backpropagation neural network was used as a prediction tool. Taking a public database the country Denmark's death data from Covid-19, with the data accumulated in the range of March 16, 2020 to May 10, 2020 being the inputs for the neural network, managing to predict for May 11, 2020 a cumulative of 209 deaths, implying a forecast of 2 deaths of error according to those that have actually been published of accumulated of 210 deaths. This result represents 98.8% effectiveness.

No abstract

By having different points in a specific space, the need arises to go through them taking as a reference the problem of the traveling agent, so also arises another problem in the journey in space, which is the risk that the paths intersect in space, therefore the research presents to solve it applies a strategy to the genetic algorithm to avoid these cuts , where the advantage of not competing all against all was taken advantage of, but from a small population the universe of cases is traversed, finding the best possible route in space avoiding these cuts. Taking into account the functions of the genetic algorithm these problems were solved using the strategy of bringing from the previous generation a pair of better individuals to the current generation. Considering if you have 50 nodes in spacewe managed to solve in 15.1 sec, generating a sequence of duration and depending on the x nodes in the linear equation of y = 0.3134x + 0.733, with R 2 = 0.978, thus also for the variance method reflects the equation y = 0,0009x 3 -0,1256x 2 + 6,1963x -36,563, con R 2 = 0,9349. Managing to find the best optimal route in finite space solving the problems found.