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The genetic algorithm (GA) is a heuristic optimization method which operates through randomized search. The set of possible solutions for the optimization problem is considered as apopulation ofindividuals. The degree of adaptation of an individual to its environment is specified by itsfitness.

The coordinates of an individual in the search space are represented bychromosomes, in essence a set of character strings. Agene is a subsection of a chromosome which encodes the value of a single parameter being optimized. Typical encodings for a gene could bebinary orinteger.

Through simulation of the evolutionary operationsrecombination,mutation, andselection new generations of search points are found that show a higher average fitness than their ancestors.

According to thecomp.ai.geneticFAQ it cannot be stressed too strongly that aGA is not a pure random search for a solution to a problem. AGA 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                          |+===+=====================================+