SELECCIóN DETERMINíSTICA Y CRUCE ANULAR EN ALGORITMOS GENéTICOS: APLICACIóN A LA PLANIFICACIóN DE UNIDADES TéRMICAS DE GENERACIóN

one of the fundamental problems of genetic algorithms (ga) is the premature convergence to a local optimum due mainly to the fact that the best individuals tend to take over the genetic characteristics of the population, reducing diversity. moreover, when the chromosome has a linear representation, the crossover is sensitive to the chromosome codification, as its head and tail change with a very low probability, only if they are affected by mutation. in this work we propose an approach to the problem of thermal generating units scheduling, using a ga which combines a deterministic selection operator with an annular crossover operator to generate new individuals. both mechanisms allow greater diversity of the population and more independence from the codification of the chromosome. these operators guarantee the convergence to an optimal solution, although there is no guarantee of rapid convergence. however, the results show that these operators have a faster convergence and reach a better performance when compared to standard ga operators.