From 1a46936216734a2a8e5abe852505a67a5bba1f4a Mon Sep 17 00:00:00 2001
From: Sam Hadow <sam.hadow@inbox.lv>
Date: Fri, 18 Oct 2024 11:37:16 +0200
Subject: [PATCH] genetic algorithm implementation

---
 genetic-algorithm.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/genetic-algorithm.py b/genetic-algorithm.py
index 8501840..928211f 100644
--- a/genetic-algorithm.py
+++ b/genetic-algorithm.py
@@ -18,3 +18,27 @@ 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
+
+def genetic(problem_data, k, popsize, mutation_chance, stop, reproduce):
+    population = generate_initial_population(problem_data, k, popsize)
+    population.sort(key = lambda x : fitness(x, problem_data, k), reverse=True)
+    loop = 0
+    while loop<stop:
+        best = fitness(population[-1], problem_data, k)
+        new = []
+        for _ in range(reproduce):
+            parents = random.choices(population, cum_weights=[1]*popsize, k=2)
+            x = random.random()
+            offspring = crossover(*parents)
+            new.append(mutation(offspring, k) if mutation_chance>x else offspring)
+        population = sorted(population + new, key=lambda x: fitness(x, problem_data, k), reverse=True)[-4:]
+
+        new_best = fitness(population[-1], problem_data, k)
+        loop = loop + 1 if best<=new_best else 0
+        best = new_best
+
+    return(population[-1])
+
+ans = genetic(tasks, 2, 4, 0.2, 10, 4)
+print(ans)
+print(fitness(ans, tasks, 2))