Force non-zero hyp evolution weights `w` (#3748)

train.py

@@ -608,7 +608,7 @@ def main(opt):
                 x = np.loadtxt('evolve.txt', ndmin=2)
                 n = min(5, len(x))  # number of previous results to consider
                 x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min()  # weights
+                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum > 0)
                 if parent == 'single' or len(x) == 1:
                     # x = x[random.randint(0, n - 1)]  # random selection
                     x = x[random.choices(range(n), weights=w)[0]]  # weighted selection