Set reorder=True per default.

cgp/ea/mu_plus_lambda.py

@@ -26,7 +26,6 @@ def __init__(
         n_processes: int = 1,
         local_search: Optional[Callable[[IndividualBase], None]] = None,
         k_local_search: Optional[int] = None,
-        reorder_genome: bool = False,
         hurdle_percentile: List = [0.0],
     ):
         """Init function
@@ -50,13 +49,6 @@ def __init__(
         k_local_search : int
             Number of individuals in the whole population (parents +
             offsprings) to apply local search to.
-       reorder_genome : bool, optional
-            Whether genome reordering should be applied.
-            Reorder shuffles the genotype of an individual without changing its phenotype,
-            thereby contributing to neutral drift through the genotypic search space.
-            If True, reorder is applied to each parents genome at every generation
-            before creating offsprings.
-            Defaults to True.
         hurdle_percentile : List[float], optional
             Specifies which percentile of individuals passes the respective
             hurdle, i.e., which individuals are evaluated on the next objective
@@ -75,7 +67,6 @@ def __init__(
         self.n_processes = n_processes
         self.local_search = local_search
         self.k_local_search = k_local_search
-        self.reorder_genome = reorder_genome
         self.hurdle_percentile = hurdle_percentile
 
         self.process_pool: Optional["mp.pool.Pool"]
@@ -127,7 +118,7 @@ def step(
             Modified population with new parents.
         """
 
-        if self.reorder_genome:
+        if pop.reorder_genome_bool:
             pop.reorder_genome()
 
         offsprings = self._create_new_offspring_generation(pop)

cgp/population.py

@@ -17,6 +17,7 @@ def __init__(
         seed: int = 1234,
         genome_params: Optional[Union[dict, List[dict]]] = None,
         individual_init: Optional[Callable[[IndividualBase], IndividualBase]] = None,
+        reorder_genome_bool: bool = True,
     ) -> None:
         """Init function.
 
@@ -32,6 +33,13 @@ def __init__(
         individual_init: callable, optional
             If not None, called for each individual of the initial
             parent population, for example, to set the dna of parents. Defaults to None.
+        reorder_genome_bool : bool, optional
+            Whether genome reordering should be applied.
+            Reorder shuffles the genotype of an individual without changing its phenotype,
+            thereby contributing to neutral drift through the genotypic search space.
+            If True, reorder is applied to each parents genome at every generation
+            in the evolutionary algorithm before creating offsprings.
+            Defaults to True.
         """
         self.n_parents = n_parents  # number of individuals in parent population
 
@@ -52,6 +60,27 @@ def __init__(
 
         self._generate_random_parent_population(individual_init)
 
+        self.reorder_genome_bool = reorder_genome_bool
+
+        # check if the reorder default is incompatible and in case set it to False
+        if isinstance(genome_params, dict):
+            if ("n_rows" in genome_params and genome_params["n_rows"] != 1) or (
+                "levels_back" in genome_params
+                and "n_columns" in genome_params
+                and genome_params["levels_back"] != genome_params["n_columns"]
+            ):
+
+                self.reorder_genome_bool = False
+
+        elif isinstance(self._genome_params, list):
+            for genome_param in self._genome_params:
+                if ("n_rows" in genome_param and genome_param["n_rows"] != 1) or (
+                    "levels_back" in genome_param
+                    and "n_columns" in genome_param
+                    and genome_param["levels_back"] != genome_param["n_columns"]
+                ):
+                    self.reorder_genome_bool = False
+
     @property
     def champion(self) -> IndividualBase:
         """Return parent with the highest fitness.
@@ -133,5 +162,6 @@ def reorder_genome(self) -> None:
         ---------
         None
         """
+        assert self.reorder_genome_bool
         for parent in self.parents:
             parent.reorder_genome(self.rng)

examples/example_reorder.py

@@ -74,7 +74,7 @@ def objective(individual):
 # evolutionary algorithms without (default) and with genome reordering.
 
 
-population_params = {"n_parents": 1, "seed": 818821}
+population_params = {"n_parents": 1, "reorder_genome_bool": True, "seed": 818821}
 
 genome_params = {
     "n_inputs": 1,
@@ -90,7 +90,6 @@ def objective(individual):
     "n_offsprings": 4,
     "mutation_rate": 0.03,
     "n_processes": 2,
-    "reorder_genome": True,
 }
 
 evolve_params = {"max_generations": int(args["--max-generations"]), "termination_fitness": 0.0}