paulin/EasyGA
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added function decorator

Browse Source
This commit is contained in:
SimpleArt
2020-11-19 21:47:45 -05:00
parent 652e4a4b5b
commit a8b38dc800
1 changed files with 21 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
src/crossover/crossover_methods.py
View File

@ -43,6 +43,14 @@ class Crossover_Methods:
class Individual:
"""Methods for crossing parents."""
def __values_to_chromosome(crossover_method):
def helper(ga, parent_1, parent_2):
return ga.make_chromosome([
ga.make_gene(value)
for value in crossover_method(ga, parent_1, parent_2)])
return helper
def single_point(ga, parent_1, parent_2):
"""Cross two parents by swapping genes at one random point."""
@ -66,17 +74,18 @@ class Crossover_Methods:
class Arithmetic:
"""Crossover methods for numerical genes."""
@Individual._Individual__values_to_chromosome
def int_random(ga, parent_1, parent_2):
"""Cross two parents by taking a random integer value between each of the genes."""
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.randint(*sorted([value_1, value_2]))) # by randomly selecting integer genes between
for value_1, value_2 in zip(value_list_1, value_list_2)]) # each parents' genes
for value_1, value_2 in zip(value_list_1, value_list_2):
yield random.randint(*sorted([value_1, value_2]))
@Individual._Individual__values_to_chromosome
def int_weighted(ga, parent_1, parent_2):
"""Cross two parents by taking a a weighted average of the genes."""
@ -86,24 +95,22 @@ class Crossover_Methods:
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(int( # filled with new integer genes
weight*value_1+(1-weight)*value_2 # with weight% from gene 1 and
)) # (100-weight)% from gene 2
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes
for value_1, value_2 in zip(value_list_1, value_list_2):
yield int(weight*value_1+(1-weight)*value_2)
@Individual._Individual__values_to_chromosome
def float_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random numeric value between each of the genes."""
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.uniform([value_1, value_2])) # by randomly selecting integer genes between
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes
for value_1, value_2 in zip(value_list_1, value_list_2):
yield random.uniform([value_1, value_2])
@Individual._Individual__values_to_chromosome
def float_weighted(ga, parent_one, parent_two):
"""Cross two parents by taking a a weighted average of the genes."""
@ -112,9 +119,6 @@ class Crossover_Methods:
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new float genes
weight*value_1+(1-weight)*value_2 # with weight% from gene 1 and
) # (100-weight)% from gene 2
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes
for value_1, value_2 in zip(value_list_1, value_list_2):
yield weight*value_1+(1-weight)*value_2