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There are many ideas to escape from local optima in GA. One solution is selecting the population for the next iteration based on the probability that is defined based on the individual score. In that case, you have a chance to select a bad score individual to escape from the local optima. Another efficient solution is playing with the mutation rate to get ...


3

First of all the answer to your question is largely dependent on the problem you are trying solve, the size of your population, the size of your problem's search space and the rest of your GA's hyper-parameters such as your mutation rate. If the problem has a large search space, then applying the elites strategy you described above will most likely cause ...


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