Use pro_m=1 for polynomial mutation in ten algorithms (pro_m=1/D mutated each gene with probability 1/D²)

src/evomo/algorithms/bige.py

@@ -109,7 +109,7 @@ def step(self) -> None:
 
         # 2. Variation
         offspring = simulated_binary(self.pop[mating_idx], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(offspring, self.lb, self.ub, pro_m=1.0 / self.lb.numel(), dis_m=20.0)
+        offspring = polynomial_mutation(offspring, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
 
         # 3. Evaluation

src/evomo/algorithms/cmoea_ms.py

@@ -115,7 +115,7 @@ def step(self) -> None:
 
         if self.op_type == 1:
             off_pop = simulated_binary(parents, pro_c=1.0, dis_c=20.0)
-            off_pop = polynomial_mutation(off_pop, self.lb, self.ub, pro_m=1.0 / self.lb.numel(), dis_m=20.0)
+            off_pop = polynomial_mutation(off_pop, self.lb, self.ub, dis_m=20.0)
         else:
             # DE logic: current-to-best style or similar
             # Using parents as a base for variation

src/evomo/algorithms/cmopso.py

@@ -112,7 +112,7 @@ def step(self) -> None:
         off_pop = clamp(off_pop, self.lb, self.ub)
 
         # Polynomial Mutation
-        off_pop = polynomial_mutation(off_pop, self.lb, self.ub, pro_m=1.0 / D, dis_m=20.0)
+        off_pop = polynomial_mutation(off_pop, self.lb, self.ub, dis_m=20.0)
 
         # 4. Evaluation
         off_fit = self.evaluate(off_pop)

src/evomo/algorithms/efr_rr.py

@@ -93,7 +93,7 @@ def step(self) -> None:
 
         # Variation
         crossovered = simulated_binary(self.pop[mating_pool], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(crossovered, self.lb, self.ub, pro_m=1.0 / self.lb.numel(), dis_m=20.0)
+        offspring = polynomial_mutation(crossovered, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
 
         # 2. Evaluation

src/evomo/algorithms/knea.py

@@ -138,7 +138,7 @@ def step(self) -> None:
 
         # Variation
         crossovered = simulated_binary(self.pop[mating_pool], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(crossovered, self.lb, self.ub, pro_m=1.0 / self.pop.shape[1], dis_m=20.0)
+        offspring = polynomial_mutation(crossovered, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
 
         # 2. Evaluation

src/evomo/algorithms/maoea_css.py

@@ -160,7 +160,7 @@ def step(self) -> None:
 
         # 2. Variation
         off_pop = simulated_binary(self.pop[final_idx], pro_c=1.0, dis_c=20.0)
-        off_pop = polynomial_mutation(off_pop, self.lb, self.ub, pro_m=1.0 / self.dim, dis_m=20.0)
+        off_pop = polynomial_mutation(off_pop, self.lb, self.ub, dis_m=20.0)
         off_pop = clamp(off_pop, self.lb, self.ub)
         off_fit = self.evaluate(off_pop)
 

src/evomo/algorithms/nsga2_sdr.py

@@ -204,7 +204,7 @@ def step(self) -> None:
 
         # 2. Variation
         crossovered = simulated_binary(self.pop[mating_pool], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(crossovered, self.lb, self.ub, pro_m=1.0 / self.lb.numel(), dis_m=20.0)
+        offspring = polynomial_mutation(crossovered, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
 
         # 3. Evaluation

src/evomo/algorithms/ts_nsga2.py

@@ -86,7 +86,7 @@ def step(self) -> None:
         """
         mating_pool = tournament_selection_multifit(self.pop_size, [self.d2, self.front_no.float()], tournament_size=2)
         offspring = simulated_binary(self.pop[mating_pool], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(offspring, self.lb, self.ub, pro_m=1.0 / self.lb.numel(), dis_m=20.0)
+        offspring = polynomial_mutation(offspring, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
 
         # 2. Evaluation

src/evomo/algorithms/ts_sparseea.py

@@ -210,7 +210,7 @@ def step(self) -> None:
         # 3. Dec Variation (SBX + PM)
         parent_pop = self.pop[mating_pool]
         off_pop_dec = simulated_binary(parent_pop, pro_c=1.0, dis_c=20.0)
-        off_pop_dec = polynomial_mutation(off_pop_dec, self.lb, self.ub, pro_m=1.0 / D, dis_m=20.0)
+        off_pop_dec = polynomial_mutation(off_pop_dec, self.lb, self.ub, dis_m=20.0)
         off_pop_dec = clamp(off_pop_dec, self.lb, self.ub)
 
         # 4. Match Operator (The Core Logic - Bug #29)

src/evomo/algorithms/wof.py

@@ -150,7 +150,7 @@ def step(self) -> None:
 
         mating_pool = tournament_selection_multifit(N, [-dist, rank.float()], tournament_size=2)
         offspring = simulated_binary(self.pop[mating_pool], pro_c=1.0, dis_c=20.0)
-        offspring = polynomial_mutation(offspring, self.lb, self.ub, pro_m=1.0 / self.dim, dis_m=20.0)
+        offspring = polynomial_mutation(offspring, self.lb, self.ub, dis_m=20.0)
         offspring = clamp(offspring, self.lb, self.ub)
         fit_off = self.evaluate(offspring)