From 93a047d2a090b16d0ad26aebafd16846406ef83b Mon Sep 17 00:00:00 2001
From: Sepehr Madani <ssepehrmadani@gmail.com>
Date: Sat, 25 Jul 2020 23:42:29 -0400
Subject: [PATCH] Added GA with buckets
---
algorithms/genetic_bucket_algorithm.py | 98 ++++++++++++++++++++++++++
1 file changed, 98 insertions(+)
create mode 100644 algorithms/genetic_bucket_algorithm.py
diff --git a/algorithms/genetic_bucket_algorithm.py b/algorithms/genetic_bucket_algorithm.py
new file mode 100644
index 0000000..87bc1e0
--- /dev/null
+++ b/algorithms/genetic_bucket_algorithm.py
@@ -0,0 +1,98 @@
+import cmath
+import random
+from copy import deepcopy
+from math import cos, degrees, inf, log10, pi, radians, sin
+
+from utils.pattern import compute_pattern
+
+from .base_algorithm import BaseAlgorithm
+
+
+class Chromosome:
+ def __init__(self, N, bit_count):
+ self.gene = [Chromosome.new_gene(bit_count) for i in range(N)]
+ self.fitness = float("nan")
+
+ @staticmethod
+ def new_gene(bit_count):
+ return random.randrange(0, 2 ** bit_count)
+
+
+class GeneticAlgorithm(BaseAlgorithm):
+ """ Finds nulls by running a genetic algorithm on all possible
+ discrete values.
+ """
+
+ def __init__(self, options):
+ BaseAlgorithm.__init__(self, options)
+ self.main_ang = options.main_ang
+ self.sample_size = options.sample_size
+ self.null_degrees = options.null_degrees
+ self.gen_to_repeat = options.gen_to_repeat
+ self.bit_count = options.bit_count
+ self.bit_resolution = options.bit_resolution
+ self.mutation_factor = options.mutation_factor
+
+ self.check_parameters()
+
+ def check_parameters(self):
+ super().check_parameters()
+
+ def solve(self):
+ self.intialize_sample()
+ self.update_fitness()
+ self.sort_fitness()
+ for generation in range(self.gen_to_repeat):
+ for ii in range(self.sample_size // 2, self.sample_size - 1, 2):
+ p1, p2 = random.sample(range(self.sample_size // 2), 2)
+ self.crossover(p1, p2, ii, ii + 1)
+ self.mutate_sample()
+ self.update_fitness()
+ self.sort_fitness()
+
+ # print(["{:.2f}".format(x.fitness) for x in self.chromosomes[:15]])
+ return self.make_weights(self.chromosomes[0])
+
+ def mutate_sample(self):
+ for chromosome in self.chromosomes[1:]: # for all except the best chromosome
+ for idx in range(self.N):
+ if random.random() <= self.mutation_factor:
+ chromosome.gene[idx] = Chromosome.new_gene(self.bit_count)
+
+ def update_fitness(self, use_exact_angle=True):
+ for chromosome in self.chromosomes:
+ values = [
+ -20 * log10(abs(x))
+ for x in compute_pattern(
+ N=self.N,
+ k=self.k,
+ weights=self.make_weights(chromosome),
+ degrees=self.null_degrees,
+ )
+ ]
+ chromosome.fitness = min(values)
+
+ def sort_fitness(self):
+ self.chromosomes.sort(key=lambda x: x.fitness, reverse=True)
+
+ def make_weights(self, chromosome):
+ weights = []
+ for bits in chromosome.gene:
+ angle = (bits - (2 ** self.bit_count - 1) / 2) * 2 * pi / (2 ** self.bit_resolution)
+ weights.append(cmath.exp(1j * angle))
+ return weights
+
+ def crossover(self, p1, p2, c1, c2):
+ self.chromosomes[c1] = deepcopy(self.chromosomes[p1])
+ self.chromosomes[c2] = deepcopy(self.chromosomes[p2])
+ for i in range(self.N):
+ if random.random() >= 0.5:
+ self.chromosomes[c1].gene[i], self.chromosomes[c2].gene[i] = (
+ self.chromosomes[c1].gene[i],
+ self.chromosomes[c2].gene[i],
+ )
+
+ def intialize_sample(self):
+ self.chromosomes = [
+ Chromosome(self.N, self.bit_count) for i in range(self.sample_size)
+ ]
\ No newline at end of file
--
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