Outline of the Basic Genetic Algorithm

1. [Start] Generate random population of n chromosomes (suitable solutions for the problem)

2. [Fitness] Evaluate the fitness f(x) of each chromosome x in the population
3. [New population] Create a new population by repeating following steps until the new population is complete
   1. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)

   2. [Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents.
   3. [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).
   4. [Accepting] Place new offspring in a new population
4. [Replace] Use new generated population for a further run of algorithm
5. [Test] If the end condition is satisfied, stop, and return the best solution in current population
6. [Loop] Go to [Fitness]

Primer, et un post fort intéressant de Dave Thomas, sur Panda’s Thumb : Target? TARGET? We don’t need no stinkin’ Target!.