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Genetic Tuning Improvements #453
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added 6 commits
May 28, 2018 15:55
The previous implementation assumed that a search strategy's iteration consist of only one step, however it is possible that multiple steps that depend on each other (and are not full "iterations") are necessary.
Using an unscaled fitness value for selection is problematic: -Outstanding individuals take over very quickly, this leads to premature convergence. -When fitness values are close together, very litle selection pressure is applied and selection is almost uniformly random. Having slightly better fitness does not improve an individual's survival chances. -Transposing the fitness function (e.g. adding a constant value) changes the selection probabilities even though the location of the optimum (and the "shape" of the fitness) remain unchanged. Scaling the fitness function helps ameliorate those issues. Sigma scaling is used: fitness' = max(fitness - (mean_fitness - 2 * std_fitness), 0)
Stochastic Universal Sampling is an improvement upon the roulette algorithm that was previously used
Previously each generation had mu candidates and generated mu children which all survided. This meant that really bad candidates that were randomly generated would survive across generations. With this change, lambda (typically larger thatn mu) children are generated and the best mu survive. The previous behaviour is a special case in which lambda = mu.
Candidates that survive across generations need not be benchmarked again and thus no compilation and gpu jobs have to be created for them.
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@caffe2bot retest this please |
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this PR replaces #160