def make_kid(pop, n_kid):
# generate empty kid holder
kids = {'DNA': np.empty((n_kid, DNA_SIZE))}
kids['mut_strength'] = np.empty_like(kids['DNA'])
for kv, ks in zip(kids['DNA'], kids['mut_strength']):
# crossover (roughly half p1 and half p2)
p1, p2 = np.random.choice(np.arange(POP_SIZE), size=2, replace=False)
cp = np.random.randint(0, 2, DNA_SIZE, dtype=np.bool) # crossover points
kv[cp] = pop['DNA'][p1, cp]
kv[~cp] = pop['DNA'][p2, ~cp]
ks[cp] = pop['mut_strength'][p1, cp]
ks[~cp] = pop['mut_strength'][p2, ~cp]
# mutate (change DNA based on normal distribution)
ks[:] = np.maximum(ks + (np.random.rand(*ks.shape)-0.5), 0.) # must > 0
kv += ks * np.random.randn(*kv.shape)
kv[:] = np.clip(kv, *DNA_BOUND) # clip the mutated value
return kids
Evolution Strategy Basic.py 文件源码
python
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