Allow multiple instances to run

src/lifecycle.py

@@ -0,0 +1,32 @@
+from population import Population
+import matplotlib.pyplot as plt
+
+
+class Lifecycle(object):
+    def __init__(self, id, params):
+        self.id = id
+        self.params = params
+        self.population = Population(self.params["population_size"])
+        self.best_fit = list()
+        self.average_fit = list()
+
+    def start(self):
+        for epoch in range(0, self.params["iter"]):
+            elite = round(self.params["elite"] * self.params["population_size"])
+            crossover = round((self.params["crossover"]
+                               * self.params["population_size"]) / 2)
+            self.population.advance_generation(elite, crossover)
+            self.best_fit.append(self.population.best_fitness())
+            self.average_fit.append(self.population.avg_fitness())
+
+    def best_member(self):
+        return self.population.best_member()
+
+    def generate_graph(self, show=False, location=None):
+        plt.plot(self.best_fit)
+        plt.xlabel("Epoch")
+        plt.ylabel("Best fitness")
+        if show:
+            plt.show()
+        if location is not None:
+            plt.savefig(location)

src/main.py

@@ -1,38 +1,16 @@
 import os
-import argparse
-from population import Population
-from time import sleep
-import matplotlib.pyplot as plt
+from lifecycle import Lifecycle
 
 
 def cls():
     os.system('cls' if os.name == 'nt' else 'clear')
 
 
-best_fit = list()
+ga = Lifecycle(1, {'population_size': 10,
+                   'elite': 0.1,
+                   'crossover': 0.6,
+                   'iter': 1000})
 
-args = argparse.ArgumentParser()
-args.add_argument("pop", help="Population size", type=int)
-args.add_argument("iter", help="Iterations", type=int)
-args.add_argument("-w", "--wait", help="Time in seconds to wait between iterations", type=int, default=0)
-args = args.parse_args()
+ga.start()
 
-pop = Population(args.pop)
-
-for i in range(0, args.iter):
-    cls()
-    print("Epoch: %d " % i)
-    elite = round(args.pop * .1)
-    crossover = round((args.pop * .8) / 2)
-    pop.advance_generation(elite, crossover)
-    best_fit.append(pop.best_fitness().fitness())
-    #input("...")
-    sleep(args.wait)
-
-print("Best: ")
-print(pop.best_fitness())
-
-plt.plot(best_fit)
-plt.xlabel("Epoch")
-plt.ylabel("Best fitness")
-plt.show()
+ga.generate_graph(show=True)

src/population.py

@@ -39,6 +39,9 @@ class Population(object):
         return float(self.total_fitness() / len(self.members))
 
     def best_fitness(self):
+        return self.best_member().fitness()
+
+    def best_member(self):
         best_member = self.members[0]
 
         for member in self.members:
@@ -73,8 +76,8 @@ class Population(object):
         new_generation = list()
 
         # elitism
-        #for member in self.elite(n_elite):
-            #new_generation.append(member)
+        for member in self.elite(n_elite):
+            new_generation.append(member)
 
         # parent
         for ind in range(0, n_crossover):
@@ -89,7 +92,7 @@ class Population(object):
 
         self.members = new_generation
 
-        self.mutate(2)
+        self.mutate(20)
 
     def remove_member(self, member):
         self.members.remove(member)