In artificial intelligence, genetic algorithms are part of the class of evolutionary algorithms. The characteristic of the latter is the finding of solutions to problems using techniques borrowed from natural evolution. The search for a solution to a problem is entrusted to an iterative process that selects and recombines more and more refined solutions until a criterion of optimality is reached. In a genetic algorithm, the population of solutions is pushed toward a given objective by the evolutionary pressure.
In the following diagram is shown a flowchart of a genetic algorithm:
Evolutionary algorithm is obtained through a particular function, called the fitness function, which is able to synthesize the quality of the solution in a single parameter. Each solution consists of a set of genes. These genes take part in the recombination...
