{"shas":[["ade1d2c40ea8b20f2ac3bc7f0105f1b0687fd68a","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","simple change; moved eval function from lambda to first-class def to allow multi processing support","Blaine Booher"],["1ca74aa2f159769ef27c25828c5b326f6da5b086","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","Adding readme, setting up repo","Blaine Booher"],["f94c321633193b167db9bb074429be867f709fac","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","Added support for a new mutator similar to Gaussian but called GaussianGradient\n\nThe following four mutators are new:\nG1DListMutatorRealGaussianGradient\nG1DListMutatorIntegerGaussianGradient\nG2DListMutatorRealGaussianGradient\nG2DListMutatorIntegerGaussianGradient\n\nThe main difference between Gaussian and GaussianGradient is that\nGaussianGradient uses a multiplicative modification rather than an additive.\n\nGaussianGradient's mu and sigma are absolute, not relative. So if the default\nvalues of mu=2 and std=10 (why not mu=0?) are used, the random gaussian number\nis a flat number around 2. If we're working on a huge range, like say 0-100000,\nthis is a very small drift.\n\nGaussianGradient uses mu=1.0 and std=0.1 to generate a number around 1.0. This\nis then *multiplied* by the gene to provide subtle drift regardless of how\nlarge the range is.\n\n2 new constants added, Mu and Sigma for the GaussianGradient routines.","Blaine Booher"],["f048b94ce4431338052b0904604e42b1efddab4b","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","Added GaussianGradient mutator which is useful for mutations that are relative\nto the gene, not absolute like it currently is.\n\nDefault mean = 1.0\nDefault std = 0.0333\n\nThis means that the values will be multiplied by 1.0 with a std of 0.0333. In\nother words, 68% of the time, the multiplier will be within +/- 0.999 (10%) of\nthe gene value.","Blaine Booher"],["1e1f0cc6fd81a4205e1972d1acbc226416acc901","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","modified setup.py so it works with pypy, otherwise it is identical","Blaine Booher"],["e8d3876b1dc5a31b952fb6931b48fca6c990651b","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","added some additional references to SBX crossover","Blaine Booher"],["98247b71aab78df91fa1df5435e6c60d00dfe991","85fa852c21b238ff6a5a2aa626740b219499625e@gmail.com","for some reason my pyevolev_graph.py was in dos format? fixed to unix","Blaine Booher"],["855b096c05bc9d12eaef7da757b5086ccb3427da","c9d54241c284e1fcfdf6560f6c1075a11dae39dd@gmail.com","Merge pull request #11 from booherbg/master\n\nMerging the pull request from Blaine, adding the Gradient Gaussian mutators, we should change this later to adapt the older mutation methods.","Christian S. Perone"]],"ref":"refs/heads/master","size":8,"head":"855b096c05bc9d12eaef7da757b5086ccb3427da","push_id":42000459}