Penerapan Algoritma Genetika Pada Permasalahan Matematika

Genetic algorithms to optimize in finding results from a problem. The algorithm consists of functions that must be completed with an initial random value, which is then carried out by an exchange called mutation and crossover. In its completion, the genetic algorithm has a rate function at random and crossbreeding rates so that not all initial individuals are randomly exchanged or crossed, and solved problems in mathematics such as math, liners, many tools available, but what is used in this paper is to use Matlab by doing it manually, in the sense of not use tools so that crossover and mutation results and genes in crosses can be known. The difference in the value in the mutation and crossover rate is very influential for completion genetic algorithm, if the rate is more than 1, it can cause many initial individuals to disappear which used to a solution found. Likewise with popsize, which is the initial value generated randomly, the more popsize, the greater the chance of completing a problem.