Genetic algorithms (GA) (Holland, 1975; Goldberg, 1989) are probabilistic search algorithms inspired from biology. They represent an optimization tool where their aim is to find a problem solution to a given problem. Thus, genetic algorithms try to explore the problem search space in order to find progressively potential solutions through time. Based on inheritance, natural selection, mutation, and sexual reproduction, they try to give after many generations the optimal solution in a finite time.
The evolution process of a genetic algorithm works on an encoding of the search space, represented by a set of individuals (chromosomes) that form the genetic population. Each individual, called solution, is randomly created and evaluated according to a measure of the solution quality. Once initially created the individuals undergo changes according to recombination operators (crossover, mutation). A new population is then generated improving the fitness of its individuals. The genetic algorithm steps can be summarized as follows:
Step1. Initialize (population)
Step2. Evaluate (population)
Step3. Repeat
3.1 Selection (population)
3.2 Crossover (population)
3.3 Mutate (population)
3.4 Evaluate (population)
Step4. Until (stopping condition is satisfied)