Update main.py

main.py

@@ -122,10 +122,10 @@ def run_optimizations_without_ml():
 
 def run_genetic_algorithm():
     print("Running Genetic Algorithm (GA)...")
-    population = [{k: random.choice(v) if isinstance(v, list) else random.uniform(*v) for k,v in OPTIMIZATION_SPACE.items()} for _ in range(50)]
+    population = [{k: random.choice(v) if isinstance(v, list) else random.uniform(*v) for k,v in OPTIMIZATION_SPACE.items()} for _ in range(40)]
     best_overall_individual = None
     best_overall_fitness = -float('inf')
-    for _ in range(100):
+    for _ in range(70):
         fitnesses = [calculate_project_kpis(**ind)['irr'] for ind in population]
         if max(fitnesses) > best_overall_fitness:
             best_overall_fitness = max(fitnesses)
@@ -154,7 +154,7 @@ def run_bayesian_optimization():
     @use_named_args(skopt_space)
     def objective(**params):
         return -calculate_project_kpis(**params)['irr']
-    res = gp_minimize(objective, skopt_space, n_calls=100, random_state=42, n_initial_points=20)
+    res = gp_minimize(objective, skopt_space, n_calls=80, random_state=42, n_initial_points=20)
     best_params = {space.name: val for space, val in zip(skopt_space, res.x)}
     kpis = calculate_project_kpis(**best_params)
     return {"Method": "Bayesian Opt", **kpis, "Params": best_params}
@@ -171,7 +171,7 @@ def run_optuna_direct():
         }
         return calculate_project_kpis(**params)['irr']
     study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=200, n_jobs=-1)
+    study.optimize(objective, n_trials=150, n_jobs=-1)
     kpis = calculate_project_kpis(**study.best_params)
     return {"Method": "Optuna (TPE - Direct)", **kpis, "Params": study.best_params}