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Autotuning improvements #501
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nicolasvasilache
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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
This is needed following the changes to the build system.
It is too slow to converge and does not yield perf gains
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thetheodor
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Jun 12, 2018
| @@ -166,6 +195,7 @@ GeneticSearch::GeneticSearch( | |||
| lastBestConf(confs[0]), | |||
| numGenerations(numGenerations), | |||
| maxPopulationSize(populationSize), | |||
| matingPoolSize(populationSize * 3), | |||
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In one of the subsequent commits I introduced a separate flag for the mating pool size (instead of hardcoding populationSize * 3).
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ftynse
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Sep 6, 2018
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This PR isolates a small subset from @ttheodor's #453.
I took the commits that look independent, fix clear bugs or implement simple new behavior and do not touch autotuner-inl.h (which underwent some changes related to early exit recently and for which I need to look deeper).