diff --git a/src/akkudoktoreos/class_ems.py b/src/akkudoktoreos/class_ems.py
index e682e18..dfa6e67 100644
--- a/src/akkudoktoreos/class_ems.py
+++ b/src/akkudoktoreos/class_ems.py
@@ -82,10 +82,10 @@ class EnergieManagementSystem:
         akku_soc_pro_stunde[0] = self.akku.ladezustand_in_prozent()
         if self.eauto:
             eauto_soc_pro_stunde[0] = self.eauto.ladezustand_in_prozent()
-       
-        for stunde in range(start_stunde + 1, ende):
+        
+        for stunde in range(start_stunde , ende):
             stunde_since_now = stunde - start_stunde
-
+            
             # Accumulate loads and PV generation
             verbrauch = self.gesamtlast[stunde]
             verluste_wh_pro_stunde[stunde_since_now] = 0.0
diff --git a/src/akkudoktoreos/class_optimize.py b/src/akkudoktoreos/class_optimize.py
index c330507..6f5791c 100644
--- a/src/akkudoktoreos/class_optimize.py
+++ b/src/akkudoktoreos/class_optimize.py
@@ -219,13 +219,13 @@ class optimization_problem:
         # Register separate mutation functions for each type of value:
         # - Discharge state mutation (-5, 0, 1)
         if self.optimize_dc_charge:
-            self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.1)
+            self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=8, indpb=0.2)
         else:
-            self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=6, indpb=0.1)
+            self.toolbox.register("mutate_charge_discharge", tools.mutUniformInt, low=0, up=6, indpb=0.2)
         # - Float mutation for EV charging values
-        self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(possible_ev_charge_currents) - 1, indpb=0.1)
+        self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(possible_ev_charge_currents) - 1, indpb=0.2)
         # - Start hour mutation for household devices
-        self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.1)
+        self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.2)
 
         # Register custom mutation function
         self.toolbox.register("mutate", self.mutate)
@@ -426,7 +426,7 @@ class optimization_problem:
             "evaluate",
             lambda ind: self.evaluate(ind, ems, parameter, start_hour, worst_case),
         )
-        start_solution, extra_data = self.optimize(parameter["start_solution"], ngen=ngen)
+        start_solution, extra_data = self.optimize(parameter["start_solution"], ngen=ngen) # 
 
         # Perform final evaluation on the best solution
         o = self.evaluate_inner(start_solution, ems, start_hour)