Genetic algorithm is a heuristic search algorithm based on the mechanism of biological evolution. On the evolution of biological diversity is the variation among individual organisms of the chromosome. Variations of this chromosome will affect the rate of reproduction and level of ability of organisms to stay alive.

In these algorithms, search techniques performed well on a number of possible solutions are known as populations. Individuals who present in a population referred to as chromosomes. This chromosome is a solution that was shaped symbol. Initial population is built randomly, while the next population is the result of the evolution of chromosomes through the iterations are referred to as generations. In each generation, chromosomes are going through the evaluation process by using a measuring instrument called a fitness function. The fitness of a chromosome will show the quality of the chromosomes in the population. The next generation is known as a child (offspring) formed from the combination of two chromosomes present generation that acts as a parent (the parent) by crossing operator (crossover). In addition to crossing operators, a chromosome can also be modified by using mutation operators. Population new generation is formed by selecting the fitness of the parent chromosome (parent) and child fitness values of chromosomes (offspring), and reject the other chromosomes so that the population size (number of chromosomes in a population) constant. After going through several generations, then the algorithm will converge to the best chromosome.

Let P (generation) is a population of one generation, then in a simple genetic algorithm consists of the steps:
1). Generation = 0 (early generations).
2). Initialize the initial population, P (generation), at random.
3). Evaluate the fitness value of each individual in P (generation).
4). Do the following steps to achieve maximum generation of generation :
a. generation = generation +1 (plus generation).
b. Selection of this population to get the candidate parent, P ‘(generation).
c. Perform crossover on P ‘(generation).
d. Do mutations in the P ‘(generation), these mutations are optional.
e. Evaluate fitness of each individual in P ‘(generation).
f. Form a new population: P (generation) = {P (generation-1) which survive, P ‘(generation)}

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