From c9a1f6cbda44b97738ced26181db34f70115bcc6 Mon Sep 17 00:00:00 2001
From: Sepehr Madani <ssepehrmadani@gmail.com>
Date: Sun, 26 Jul 2020 11:39:14 -0400
Subject: [PATCH] Implemented AM-GM crossover
---
algorithms/genetic_bucket_algorithm.py | 55 +++++++++++++++-----------
1 file changed, 32 insertions(+), 23 deletions(-)
diff --git a/algorithms/genetic_bucket_algorithm.py b/algorithms/genetic_bucket_algorithm.py
index 87bc1e0..63e0558 100644
--- a/algorithms/genetic_bucket_algorithm.py
+++ b/algorithms/genetic_bucket_algorithm.py
@@ -18,7 +18,7 @@ class Chromosome:
return random.randrange(0, 2 ** bit_count)
-class GeneticAlgorithm(BaseAlgorithm):
+class GeneticBucketAlgorithm(BaseAlgorithm):
""" Finds nulls by running a genetic algorithm on all possible
discrete values.
"""
@@ -32,11 +32,13 @@ class GeneticAlgorithm(BaseAlgorithm):
self.bit_count = options.bit_count
self.bit_resolution = options.bit_resolution
self.mutation_factor = options.mutation_factor
+ self.buckets = [[]] * 8
self.check_parameters()
def check_parameters(self):
super().check_parameters()
+ assert len(self.null_degrees) == 1
def solve(self):
self.intialize_sample()
@@ -44,13 +46,15 @@ class GeneticAlgorithm(BaseAlgorithm):
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)
+ bucket_idx = random.randrange(4)
+ p1 = random.choice(self.buckets[bucket_idx])
+ p2 = random.choice(self.buckets[bucket_idx + 4])
+
self.crossover(p1, p2, ii, ii + 1)
- self.mutate_sample()
+ # 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):
@@ -61,36 +65,41 @@ class GeneticAlgorithm(BaseAlgorithm):
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 = -20 * log10(
+ abs(
+ compute_pattern(
+ N=self.N,
+ k=self.k,
+ weights=self.make_weights(chromosome),
+ degrees=self.null_degrees,
+ use_absolute_value=False,
+ )[0]
)
- ]
- chromosome.fitness = min(values)
+ )
+ bucket_idx = int(((cmath.phase(chromosome.fitness) + pi) / (2 * pi)) * 8) % 8
+ self.buckets[bucket_idx].append(chromosome)
def sort_fitness(self):
- self.chromosomes.sort(key=lambda x: x.fitness, reverse=True)
+ self.chromosomes.sort(key=lambda x: abs(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)
+ 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],
- )
+ for ii in range(self.N):
+ g1 = p1.gene[ii]
+ g2 = p2.gene[ii]
+ self.chromosomes[c1].gene[ii] = (g1 + g2) // 2
+ self.chromosomes[c2].gene[ii] = (g1 + g2 + 1) // 2
def intialize_sample(self):
self.chromosomes = [
--
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