The genetic algorithm operates on a population of candidate solutions.

1. Initialize the set of candidate solutions.

2. Evaluate each solution in the population.

3. Solutions that are good are allowed to reproduce, with mutations (changes to variable values) in some copies; the amount of reproduction can depend on how good a solution is. (Koza uses 9% clones of the best in the old population, 90% recombinations, and 1% mutations.)

4. Poor solutions can be eliminated to keep the total population constant.

5. If the population is dominated by one solution, stop and return that solution; otherwise, go to step 2.

Compare: hill climbing, beam search.

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