Other optimization methods include reinforcement learning methods, where only one model is trained. This model is evaluated for fitness, and an algorithm like back propagation is used to improve the fitness of the model. This is done iteratively until the model is sufficiently fit. This can be a better fit for some problems as it does not rely on randomness as much as genetic algorithms.
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