Fix example parameterisation, clean up docstrings

examples/example_reorder.py

@@ -70,8 +70,8 @@ def objective(individual):
 
 # %%
 # Next, we set up the evolutionary search. We first define the
-# parameters for the population, the genome of individuals, and the
-# evolutionary algorithm.
+# parameters for the population, the genome of individuals, and two
+# evolutionary algorithms without (default) and with genome reordering.
 
 
 population_params = {"n_parents": 1, "mutation_rate": 0.03, "seed": 818821}
@@ -86,6 +86,12 @@ def objective(individual):
 }
 
 ea_params = {"n_offsprings": 4, "tournament_size": 2, "n_processes": 2}
+ea_params_with_reorder = {
+    "n_offsprings": 4,
+    "tournament_size": 2,
+    "n_processes": 2,
+    "reorder_genome": True,
+}
 
 evolve_params = {"max_generations": int(args["--max-generations"]), "min_fitness": 0.0}
 
@@ -95,8 +101,9 @@ def objective(individual):
 pop_with_reorder = cgp.Population(**population_params, genome_params=genome_params)
 
 # %%
-# and an instance of the (mu + lambda) evolutionary algorithm
+# and two instances of the (mu + lambda) evolutionary algorithm
 ea = cgp.ea.MuPlusLambda(**ea_params)
+ea_with_reorder = cgp.ea.MuPlusLambda(**ea_params_with_reorder)
 
 # %%
 # We define two callbacks for recording of fitness over generations
@@ -125,17 +132,16 @@ def recording_callback_with_reorder(pop):
 cgp.evolve(
     pop_with_reorder,
     objective,
-    ea,
+    ea_with_reorder,
     **evolve_params,
     print_progress=True,
     callback=recording_callback_with_reorder,
-    reorder_genome=True,
 )
 
 
 # %%
-# After finishing the evolution, we plot the result and log the final
-# evolved expression.
+# After finishing the evolution, we plot the evolution of the fittest individual
+# with and without genome reordering
 
 
 width = 9.0