An optimization technique inspired by natural selection, used to tune strategy parameters. In algorithm design, a genetic algorithm (GA) iteratively “evolves” a population of parameter sets (like moving-average lengths or stop levels) by selecting the fittest combinations and combining them (crossover/mutation). In Forex robots, GAs automate the search for best-performing rule parameters on historical data. For example, a GA might optimize a moving-average crossover strategy by testing many MA pairs and retaining those yielding highest simulated profit.

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