Add a Butterfly-based implementation of genetic

algorithms/genetic_butterfly_algorithm.py

+from random import randrange, random, choice, sample
+from cmath import exp, phase
+from math import log10, pi, nan, cos, sin
+
+from utils.pattern import compute_pattern, compute_single_pattern
+
+from .genetic_algorithm import GeneticAlgorithm, Chromosome
+from .butterfly_algorithm import ButterflyAlgorithm
+
+
+class GeneticWithButterflyAlgorithm(GeneticAlgorithm):
+    """ Finds nulls by running a genetic algorithm on all possible
+    discrete values. Uses the Butterfly algorithm to create a base
+    chromosome, from which the entire population is created.
+    """
+
+    def __init__(self, options):
+        super().__init__(options)
+        butterfly_alg = ButterflyAlgorithm(options)
+        weights, score = butterfly_alg.solve()
+        self.base_weights = [x for x in weights]
+        pass
+
+    def initialize_sample(self):
+        self.generations = 0
+        self.chromosomes.clear()
+        if self.overwrite_mutations:
+            self.chromosomes = [Chromosome(self.base_weights, shufflize=False)] + [
+                Chromosome(self.base_weights) for _ in range(self.sample_size - 1)
+            ]
+        else:
+            self.chromosomes = [Chromosome(self.base_weights, shufflize=False)] + [
+                Chromosome(self.base_weights) for _ in range(self.sample_size * 2 - 2)
+            ]
+
+        if self.buckets is not None:
+            self.initialize_buckets()
+
+
+# TODO: Find what overloading I missed from Genetic. The min score for this alg cannot be worse than the base_weights