Domain update

Can set the domain to either a range or a list of values.

src/EasyGA.py

@@ -12,6 +12,7 @@ from initialization.random_initialization import random_initialization
 class GA:
     def __init__(self):
         # Default variables
+        self.domain = range(1, 100)
         self.population = None
         self.generations = 3
         self.chromosome_length = 4
@@ -27,13 +28,15 @@ class GA:
         #self.termination_impl = GenerationTermination(Total_generations)
         #self.evaluation_impl = TestEvaluation()
 
-
     def initialize(self):
+        if isinstance(self.domain, range):
+            self.domain = [x/float(100) for x in range(int(min(self.domain)*100), int(max(self.domain)*100))]
         # Create the first population
         self.population = self.initialization_impl(
         self.population_size,
         self.chromosome_length,
-        self.gene_function_impl)
+        self.gene_function_impl,
+        self.domain)
 
     def evolve():
         # If you just want to evolve through all generations

src/initialization/gene_function/gene_random.py

@@ -1,13 +1,5 @@
 # Imported library
 import random
 
-def check_values(low,high):
+def random_gene(domain):
-    #Check to make sure its not less then zero
+    return domain[random.randint(0, len(domain)-1)]
-    assert low > 0 , "The random gene low can not be less then zero"
-    # Check to make sure the high value is not
-    # lower than or equal to low and not 0.
-    assert high > low , "High value can not be smaller then low value"
-    assert high != 0, "High value can not be zero"
-
-def random_gene():
-    return random.randint(1,100)

src/initialization/population_structure/population.py

@@ -36,8 +36,7 @@ class population:
         self.fitness = fitness
 
     def __repr__(self):
-        for index in range(len(self.chromosomes)):
+        return ''.join([chromosome.__repr__() for chromosome in self.chromosomes])
-            return f'{self.chromosomes[index]}'
 
     def print_all(self):
         # Ex .Current population

src/initialization/random_initialization.py

@@ -3,7 +3,7 @@ from .population_structure.population import population as create_population
 from .chromosome_structure.chromosome import chromosome as create_chromosome
 from .gene_structure.gene import gene as create_gene
 
-def random_initialization(chromosome_length,population_size,gene_function):
+def random_initialization(chromosome_length, population_size, gene_function, domain):
     # Create the population object
     population = create_population()
     # Fill the population with chromosomes
@@ -11,6 +11,6 @@ def random_initialization(chromosome_length,population_size,gene_function):
         chromosome = create_chromosome()
         #Fill the Chromosome with genes
         for j in range(chromosome_length):
-            chromosome.add_gene(create_gene(gene_function()))
+            chromosome.add_gene(create_gene(gene_function(domain)))
         population.add_chromosome(chromosome)
     return population

src/run_testing.py

@@ -3,18 +3,12 @@ import EasyGA
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-#Creating a gene with no fitness
+# input domain
-gene1 = ga.make_gene("Im a gene")
+#ga.domain = range(3, 10)
-gene2 = ga.make_gene("Im also a gene")
+ga.domain = ['left', 'right']
-#Creating a Chromosome with no genes
-chromosome = ga.make_chromosome()
-chromosome.add_gene(gene1)
-chromosome.add_gene(gene2)
-# Creating a populaiton
-populaiton = ga.make_population()
-populaiton.add_chromosome(chromosome)
 
-print(gene1)
+# initialize random population
-print(chromosome)
+ga.initialize()
-print(populaiton)
+
-populaiton.print_all()
+# Print population
+ga.population.print_all()