initial pop, crossover and mutation functions

genetic-algorithm.py

@@ -0,0 +1,20 @@
+#/bin/python
+import random
+
+tasks = [4, 5, 8, 2, 10, 7]
+sol1=[1, 0, 1, 1, 0, 1]
+sol2=[0, 0, 1, 0, 1, 1]
+
+def generate_initial_population(problem_data, k, popsize):
+    return [[random.randint(0, k-1) for _ in range(len(problem_data))] for _ in range(popsize)]
+
+def fitness(solution, problem_data, k):
+    return max([sum(problem_data[i] for i in range(len(solution)) if solution[i] == j) for j in range(k)])
+
+def crossover(solutionA, solutionB):
+    return [solutionA[i] if i % 2 == 0 else solutionB[i] for i in range(len(solutionA))]
+
+def mutation(solution, k):
+    i = random.randint(0,len(solution)-1)
+    solution[i] = random.choice(list({i for i in range(k)} - {solution[i]}))
+    return solution