Swap out optimizer for CmaEsSampler in stab flex test

It runs faster, max_iterations controls the number
of evaluations. It also seems to get rid of the
issue of differences between fp32 and fp64 results. Will need to look into what is going on
with differential evolution.

tests/composition/pec/test_stab_flex_pec_fit.py

@@ -1,4 +1,6 @@
 import psyneulink as pnl
+
+import optuna
 import pytest
 
 from psyneulink.core.components.functions.nonstateful.fitfunctions import (
@@ -470,7 +472,7 @@ def test_stab_flex_cond_fit():
         ],
         data=data_to_fit,
         optimization_function=PECOptimizationFunction(
-            method="differential_evolution", max_iterations=1
+            method=optuna.samplers.CmaEsSampler(seed=0), max_iterations=10
         ),
         num_estimates=num_estimates,
         initial_seed=42,
@@ -485,24 +487,14 @@ def test_stab_flex_cond_fit():
     optimal_parameters = pec.optimized_parameter_values
 
     # These aren't the recovered parameters, we are doing too few trials and too few estimates to get the correct
-    # results. FP32 results are very different from FP64 results, this seems strange, we will need to investigate
-    # what is going on here. Maybe it effects the randomization somehow?
-    if pytest.helpers.llvm_current_fp_precision() == 'fp32':
-        expected_results = {
-            'Task Activations [Act1, Act2]-1.gain': 2.316350155331747,
-            'Automaticity-weighted Stimulus Input [w*S1, w*S2]-1.slope': 0.04222951406320785,
-            'DDM-1.threshold[threshold=0.7]': 0.49284829086077964,
-            'DDM-1.threshold[threshold=0.3]': 0.3469243996445839,
-            'DDM-1.non_decision_time': 0.38630189671150356
-        }
-    else:
-        expected_results = {
-            'Task Activations [Act1, Act2]-1.gain': 1.3820085411824117,
-            'Automaticity-weighted Stimulus Input [w*S1, w*S2]-1.slope': 0.014784656669402561,
-            'DDM-1.threshold[threshold=0.7]': 0.48339391888454464,
-            'DDM-1.threshold[threshold=0.3]': 0.3098280374938238,
-            'DDM-1.non_decision_time': 0.1278625281322982
-        }
+    # results.
+    expected_results = {
+        'Task Activations [Act1, Act2]-1.gain': 3.87419,
+        'Automaticity-weighted Stimulus Input [w*S1, w*S2]-1.slope': 0.0125,
+        'DDM-1.threshold[threshold=0.7]': 0.30939,
+        'DDM-1.threshold[threshold=0.3]': 0.22168,
+        'DDM-1.non_decision_time': 0.28659999999999997
+    }
 
     for key, value in expected_results.items():
         np.testing.assert_allclose(optimal_parameters[key], value, rtol=1e-6)