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A genetic algorithm (GA) is a heuristic optimization method that operates through determined, randomized search. The set of possible solutions for the optimization problem is considered as a population of individuals. The degree of adaption of an individual to its environment is specified by its fitness.
The coordinates of an individual in the search space are represented by chromosomes, in essence a set of character strings. A gene is a subsection of a chromosome which encodes the value of a single parameter being optimized. Typical encodings for a gene could be binary or integer.
Through simulation of the evolutionary operations recombination, mutation, and selection, new generations of search points are found that show a higher average fitness than their ancestors.
According to the "comp.ai.genetic" FAQ, a GA is not a pure random search for a solution to a problem. A GA uses stochastic processes, but the result is distinctly non-random (better than random).
+=========================================+
|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<|
+=========================================+
| INITIALIZE t := 0 |
+=========================================+
| INITIALIZE P(t) |
+=========================================+
| evaluate FITNESS of P(t) |
+=========================================+
| while not STOPPING CRITERION do |
| +-------------------------------------+
| | P'(t) := RECOMBINATION{P(t)} |
| +-------------------------------------+
| | P''(t) := MUTATION{P'(t)} |
| +-------------------------------------+
| | P(t+1) := SELECTION{P''(t) + P(t)} |
| +-------------------------------------+
| | evaluate FITNESS of P''(t) |
| +-------------------------------------+
| | t := t + 1 |
+===+=====================================+ |
Figure 12-1. Structured Diagram of a Genetic Algorithm