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Evolutionary algorithms mimic Nature by working on a population of possible solutions to a problem, changing them randomly, combining them with each other, selecting them according to how well they solve the problem.
- Do it for several generations.
- That's it!
There are many evolutionary algorithms: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, and minor variants: estimation of distribution algorithms, grammatical evolution, gene expression programming... Since genetic algorithms are the most widely known, evolutionary algorithms are sometimes called just genetic algorithms.