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Can boosting be classified as a genetic algorithm or as an instance of simulated annealing?

Or, is it a completely different paradigm?

Essentially, I'm trying to rectify discrete optimization methods with their counter parts in machine learning.

Thanks!

Also, if you can provide a reference, that'd be great.

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It is a completely different paradigm. Boosting involves using a group of outcomes to achieve a final result instead of just one. The outcomes are separately weighted so that more accurate ones have a bigger influence on the final result. It is hard to understand simulated annealing starting from simulated annealing. I recommend looking at articles on MCMC first, or understand the idea of hill-climbing (iteratively moving to the next best solution). GAs are once again different, but the most intuitive, since they are pretty true to their name.

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  • $\begingroup$ Okay, got it. And yeah, I'm familiar with MCMC, but since the algorithms accomplish similar tasks and do aggregate local solutions, I was wondering one can be rephrased in terms of the others. But, now I see the subtleties. $\endgroup$
    – user237393
    Commented May 4, 2015 at 4:15

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