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For questions related to local search algorithms used in AI (e.g. 2-opt or hill climbing).
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Why is a mix of greedy and random usually "best" for stochastic local search?
I read that a mix of "greedy" and "random" are ideal for stochastic local search (SLS), but I'm not sure why. It mentioned that the greedy finds the local minima and the randomness avoids getting trap …