modified by adaptive mutation&crossove(ILM/DHC method, DHM/ILC method…

ravenframework/Optimizers/crossOverOperators/crossovers.py

@@ -44,8 +44,6 @@ def onePointCrossover(parents,**kwargs):
                           dims=['chromosome','Gene'],
                           coords={'chromosome': np.arange(int(2*comb(nParents,2))),
                                   'Gene':kwargs['variables']})
-
-
   # defaults
   if (kwargs['crossoverProb'] == None) or ('crossoverProb' not in kwargs.keys()):
     crossoverProb = randomUtils.random(dim=1, samples=1)
@@ -54,11 +52,11 @@ def onePointCrossover(parents,**kwargs):
 
   # create children
   parentsPairs = list(combinations(parents,2))
-
   for ind,parent in enumerate(parentsPairs):
     parent = np.array(parent).reshape(2,-1) # two parents at a time
-
-    if randomUtils.random(dim=1,samples=1) <= crossoverProb:
+    new_crossoverProb=1-((2*ind)/len(children)) #ILM/DHC method
+    new_crossoverProb2=((2*ind)/len(children)) #DHM/ILC method
+    if randomUtils.random(dim=1,samples=1) <= new_crossoverProb2:
       if (kwargs['points'] == None) or ('points' not in kwargs.keys()):
         point = list([randomUtils.randomIntegers(1,nGenes-1,None)])
       elif (any(i>=nGenes-1 for i in kwargs['points'])):

ravenframework/Optimizers/mutators/mutators.py

@@ -51,7 +51,10 @@ def swapMutator(offSprings, distDict, **kwargs):
   for i in range(np.shape(offSprings)[0]):
     children[i] = offSprings[i]
     ## TODO What happens if loc1 or 2 is out of range?! should we raise an error?
-    if randomUtils.random(dim=1,samples=1)<=kwargs['mutationProb']:
+    new_mutationProb=(i/len(children)) #ILM/DHC method
+    new_mutationProb2=1-(i/len(children)) #DHM/ILC method
+    if randomUtils.random(dim=1,samples=1)<=new_mutationProb2:
+      #kwargs['mutationProb']
       # convert loc1 and loc2 in terms on cdf values
       cdf1 = distDict[offSprings.coords['Gene'].values[loc1]].cdf(float(offSprings[i,loc1].values))
       cdf2 = distDict[offSprings.coords['Gene'].values[loc2]].cdf(float(offSprings[i,loc2].values))
@@ -136,9 +139,13 @@ def randomMutator(offSprings, distDict, **kwargs):
   """
   if kwargs['locs'] is not None and 'locs' in kwargs.keys():
     raise ValueError('Locs arguments are not being used by randomMutator')
+  k=0
   for child in offSprings:
     # the mutation is performed for each child independently
-    if randomUtils.random(dim=1,samples=1)<kwargs['mutationProb']:
+    new_mutationProb=(k/len(offSprings))
+    k=k+1
+    if randomUtils.random(dim=1,samples=1)<new_mutationProb:
+      # kwargs['mutationProb']
       # sample gene location to be flipped: i.e., determine loc
       chromosomeSize = child.values.shape[0]
       loc = randomUtils.randomIntegers(0, chromosomeSize, caller=None, engine=None)
@@ -217,3 +224,15 @@ def returnInstance(cls, name):
   if name not in __mutators:
     cls.raiseAnError (IOError, "{} MECHANISM NOT IMPLEMENTED!!!!!".format(name))
   return __mutators[name]
+
+# def adaptivemutator(**kwargs):
+#   """
+#     Method designed to adaptive mutation rate:
+#     @ In, kwargs['mutationProb']
+#     @ Out, kwargs['new_mutationProb']
+#   """
+#   for i in range():
+#     new_mutationProb=1-(i/len(children))
+#     kwargs['mutationProb']=new_mutationProb
+#     print(new_mutationProb)
+#   return new_mutationProb

tests/framework/Optimizers/GeneticAlgorithms/discrete/unconstrained/GA_discreteIntWithReplacement.xml

@@ -64,6 +64,7 @@
 
       <GAparams>
         <populationSize>17</populationSize>
+        <!-- 17 -->
         <reproduction>
           <crossover type="onePointCrossover">
             <crossoverProb>0.9</crossoverProb>

tests/framework/Optimizers/GeneticAlgorithms/discrete/unconstrained/testGAMaxwoRepConvHDSM.xml

@@ -68,7 +68,7 @@
           <crossover type="onePointCrossover">
             <crossoverProb>0.8</crossoverProb>
           </crossover>
-          <mutation type="swapMutator">
+          <mutation type="randomMutator">
             <mutationProb>0.9</mutationProb>
           </mutation>
         </reproduction>