The GA is a heuristic optimization method which 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 it cannot be stressed too strongly that 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).
Structured Diagram of a GA:
---------------------------
P(t) generation of ancestors at a time t
P''(t) generation of descendants at a time t
+=========================================+
|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|
+=========================================+
| INITIALIZE t := 0 |
+=========================================+
| INITIALIZE P(t) |
+=========================================+
| evalute 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)} |
| +-------------------------------------+
| | evalute FITNESS of P''(t) |
| +-------------------------------------+
| | t := t + 1 |
+===+=====================================+
Read the upstream documentation. For corrections, first check the current version.