make the stagnation stopping criterion optional

Author: Jaquier Aurélien Tristan

bluepyopt/deapext/CMA_MO.py

@@ -90,6 +90,7 @@ def __init__(
         weight_hv=0.5,
         map_function=None,
         use_scoop=False,
+        use_stagnation_criterion=True,
     ):
         """Constructor
 
@@ -109,6 +110,8 @@ def __init__(
             map_function (map): function used to map (parallelize) the
                  evaluation function calls
             use_scoop (bool): use scoop map for parallel computation
+            use_stagnation_criterion (bool): whether to use the stagnation
+                stopping criterion on top of the maximum generation criterion
         """
 
         if offspring_size is None:
@@ -165,8 +168,11 @@ def __init__(
 
         self.stopping_conditions = [
             MaxNGen(max_ngen),
-            Stagnationv2(lambda_, self.problem_size),
         ]
+        if use_stagnation_criterion:
+            self.stopping_conditions.append(
+                Stagnationv2(lambda_, self.problem_size)
+            )
 
     def _select(self, candidates):
         """Select the best candidates of the population

bluepyopt/deapext/CMA_SO.py

@@ -59,6 +59,7 @@ def __init__(
         RandIndCreator,
         map_function=None,
         use_scoop=False,
+        use_stagnation_criterion=True,
     ):
         """Constructor
 
@@ -74,6 +75,8 @@ def __init__(
             map_function (map): function used to map (parallelize) the
                 evaluation function calls
             use_scoop (bool): use scoop map for parallel computation
+            use_stagnation_criterion (bool): whether to use the stagnation
+                stopping criterion on top of the maximum generation criterion
         """
 
         if offspring_size is None:
@@ -108,7 +111,6 @@ def __init__(
 
         self.stopping_conditions = [
             MaxNGen(max_ngen),
-            Stagnationv2(lambda_, self.problem_size),
             TolHistFun(lambda_, self.problem_size),
             EqualFunVals(lambda_, self.problem_size),
             NoEffectAxis(self.problem_size),
@@ -117,6 +119,10 @@ def __init__(
             ConditionCov(),
             NoEffectCoor(),
         ]
+        if use_stagnation_criterion:
+            self.stopping_conditions.append(
+                Stagnationv2(lambda_, self.problem_size)
+            )
 
     def update(self, population):
         """Update the current covariance matrix strategy from the

bluepyopt/deapext/optimisationsCMA.py

@@ -61,6 +61,7 @@ def __init__(
         selector_name="single_objective",
         weight_hv=0.5,
         fitness_reduce=numpy.sum,
+        use_stagnation_criterion=True,
     ):
         """Constructor
 
@@ -85,6 +86,8 @@ def __init__(
                 is computed as 1 - weight_hv.
             fitness_reduce (fcn): function used to reduce the objective values
                 to a single fitness score
+            use_stagnation_criterion (bool): whether to use the stagnation
+                stopping criterion on top of the maximum generation criterion
         """
 
         super(DEAPOptimisationCMA, self).__init__(evaluator=evaluator)
@@ -118,6 +121,8 @@ def __init__(
                 "or 'multi_objective'. Not "
                 "{}".format(self.selector_name)
             )
+        
+        self.use_stagnation_criterion = use_stagnation_criterion
 
         # Number of objective values
         self.problem_size = len(self.evaluator.params)
@@ -286,6 +291,7 @@ def run(
                 RandIndCreator=self.toolbox.RandomInd,
                 map_function=self.map_function,
                 use_scoop=self.use_scoop,
+                use_stagnation_criterion=self.use_stagnation_criterion,
             )
 
             if self.selector_name == "multi_objective":