<p>In <a href="https://github.com/mlpack/mlpack/pull/753#discussion_r73802812">src/mlpack/methods/ne/cne.hpp</a>:</p>
<pre style='color:#555'>> + // TODO: here we use uniform distribution. Can we use exponential distribution?
> + static void MutateWeightsBiased(Genome& genome, double mutateProb, double mutateSize) {
> + for (int i=0; i<genome.aLinkGenes.size(); ++i) {
> + double p = mlpack::math::Random(); // rand 0~1
> + if (p < mutateProb) {
> + double deltaW = mlpack::math::RandNormal(0, mutateSize);
> + double oldW = genome.aLinkGenes[i].Weight();
> + genome.aLinkGenes[i].Weight(oldW + deltaW);
> + }
> + }
> + }
> +
> + // Hard mutation: replace with a random value chosen from
> + // initialization prob distribution with probability p.
> + // TODO: here we use uniform distribution. Can we use exponential distribution?
> + static void MutateWeightsUnbiased(Genome& genome, double mutateProb, double mutateSize) {
</pre>
<p>Do you think, we could use a boolean to distinguish between soft and hard mutation. In this case we could reuse the basic function.</p>
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