Initialization for GA with arguments

src/run_testing.py

@@ -2,30 +2,31 @@ import random
 import EasyGA
 
 # Create the Genetic algorithm
-ga = EasyGA.GA()
-
-# Reproduce 30% of the population.
-# Mutate 20% of the population.
-# Mutate 3% of the genes in each mutated chromosome.
-ga.parent_ratio  = 0.30
-ga.chromosome_mutation_rate = 0.20
-ga.gene_mutation_rate = 0.03
-
-# Create 25 chromosomes each with 10 genes
-ga.population_size = 100
-ga.chromosome_length = 25
-
-# Create random genes from 0 to 10
-ga.gene_impl = lambda: random.randint(0, 10)
-
-# Minimize the sum of the genes
-ga.fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list())
-ga.target_fitness_type = 'min'
-
-# Terminate when a chromosome has all 0's
-ga.fitness_goal = 0
-ga.generation_goal = 150
+#   Reproduce 30% of the population.
+#   Mutate 20% of the population.
+#   Mutate 3% of the genes in each mutated chromosome.
+#   
+#   Create 25 chromosomes each with 10 genes
+#   Create random genes from 0 to 10
+#   
+#   Minimize the sum of the genes
+#   Terminate when a chromosome has all 0's
+#     or 150 generations pass
+# 
+ga = EasyGA.GA(
+    parent_ratio = 0.30,
+    chromosome_mutation_rate = 0.20,
+    gene_mutation_rate = 0.03,
+    population_size = 100,
+    chromosome_length = 25,
+    gene_impl = lambda: random.randint(0, 10),
+    fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list()),
+    target_fitness_type = 'min',
+    fitness_goal = 0,
+    generation_goal = 150
+)
 
+# Run 10 generations at a time
 while ga.active():
     ga.evolve_generation(10)
     ga.print_generation()