diff --git a/pygad.py b/pygad.py
index 4b1e2916..071658a2 100644
--- a/pygad.py
+++ b/pygad.py
@@ -1695,7 +1695,7 @@ def single_point_crossover(self, parents, offspring_size):
             # The new offspring has its second half of its genes from the second parent.
             offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
 
-            if (self.mutation_type is None) and (self.allow_duplicate_genes == False):
+            if self.allow_duplicate_genes == False:
                 if self.gene_space is None:
                     offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k],
                                                                                          min_val=self.random_mutation_min_val,
@@ -1761,7 +1761,7 @@ def two_points_crossover(self, parents, offspring_size):
             # The genes between the 2 points are copied from the second parent.
             offspring[k, crossover_point1:crossover_point2] = parents[parent2_idx, crossover_point1:crossover_point2]
 
-            if (self.mutation_type is None) and (self.allow_duplicate_genes == False):
+            if self.allow_duplicate_genes == False:
                 if self.gene_space is None:
                     offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k],
                                                                                          min_val=self.random_mutation_min_val,
@@ -1821,7 +1821,7 @@ def uniform_crossover(self, parents, offspring_size):
                     # The gene will be copied from the second parent if the current gene index is 1.
                     offspring[k, gene_idx] = parents[parent2_idx, gene_idx]
 
-            if (self.mutation_type is None) and (self.allow_duplicate_genes == False):
+            if self.allow_duplicate_genes == False:
                 if self.gene_space is None:
                     offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k],
                                                                                          min_val=self.random_mutation_min_val,
@@ -1877,7 +1877,7 @@ def scattered_crossover(self, parents, offspring_size):
             gene_sources = numpy.random.randint(0, 2, size=self.num_genes)
             offspring[k, :] = numpy.where(gene_sources == 0, parents[parent1_idx, :], parents[parent2_idx, :])
 
-            if (self.mutation_type is None) and (self.allow_duplicate_genes == False):
+            if self.allow_duplicate_genes == False:
                 if self.gene_space is None:
                     offspring[k], _, _ = self.solve_duplicate_genes_randomly(solution=offspring[k],
                                                                                          min_val=self.random_mutation_min_val,
@@ -3361,7 +3361,8 @@ def plot_fitness(self,
                     font_size=14, 
                     plot_type="plot",
                     color="#3870FF",
-                    save_dir=None):
+                    save_dir=None,
+                    show=True):
 
         """
         Creates, shows, and returns a figure that summarizes how the fitness value evolved by generation. Can only be called after completing at least 1 generation. If no generation is completed, an exception is raised.
@@ -3375,7 +3376,7 @@ def plot_fitness(self,
             plot_type: Type of the plot which can be either "plot" (default), "scatter", or "bar".
             color: Color of the plot which defaults to "#3870FF".
             save_dir: Directory to save the figure.
-
+            show: For True show the plot
         Returns the figure.
         """
 
@@ -3399,7 +3400,8 @@ def plot_fitness(self,
         if not save_dir is None:
             matplotlib.pyplot.savefig(fname=save_dir, 
                                       bbox_inches='tight')
-        matplotlib.pyplot.show()
+        if show:
+            matplotlib.pyplot.show()
 
         return fig