diff --git a/lib/galaxy/tools/__init__.py b/lib/galaxy/tools/__init__.py
index cd9cd2b5dc8..f613efdc480 100755
--- a/lib/galaxy/tools/__init__.py
+++ b/lib/galaxy/tools/__init__.py
@@ -2590,28 +2590,44 @@ class Tool( object, Dictifiable ):
         # dataset used; parameter should be the analygous dataset in the
         # current history.
         history = trans.get_history()
-        hda_source_dict = {} # Mapping from HDA in history to source HDAs.
+
+        # Create index for hdas.
+        hda_source_dict = {}
         for hda in history.datasets:
-            source_hda = hda.copied_from_history_dataset_association
-            while source_hda:
-                if source_hda.dataset.id not in hda_source_dict or source_hda.hid == hda.hid:
-                    hda_source_dict[ source_hda.dataset.id ] = hda
-                source_hda = source_hda.copied_from_history_dataset_association
+            key = '%s_%s' % (hda.hid, hda.dataset.id)
+            hda_source_dict[ hda.dataset.id ] = hda_source_dict[ key ] = hda
 
         # Ditto for dataset collections.
         hdca_source_dict = {}
         for hdca in history.dataset_collections:
-            source_hdca = hdca.copied_from_history_dataset_collection_association
-            while source_hdca:
-                if source_hdca.collection.id not in hdca_source_dict or source_hdca.hid == hdca.hid:
-                    hdca_source_dict[ source_hdca.collection.id ] = hdca
-                source_hdca = source_hdca.copied_from_history_dataset_collection_association
+            key = '%s_%s' % (hdca.hid, hdca.collection.id)
+            hdca_source_dict[ hda.collection.id ] = hdca_source_dict[ key ] = hdca
+
+        # Map dataset or collection to current history
+        def map_to_history(value):
+            id = None
+            source = None
+            if isinstance(value, trans.app.model.HistoryDatasetAssociation):
+                id = value.dataset.id
+                source = hda_source_dict
+            elif isinstance(value, trans.app.model.HistoryDatasetCollectionAssociation):
+                id = value.collection.id
+                source = hdca_source_dict
+            else:
+                return None
+            key = '%s_%s' % (value.hid, id)
+            if key in source:
+                return source[ key ]
+            elif id in source:
+                return source[ id ]
+            else:
+                return None
 
         # Unpack unvalidated values to strings, they'll be validated when the
         # form is submitted (this happens when re-running a job that was
         # initially run by a workflow)
         #This needs to be done recursively through grouping parameters
-        def rerun_callback( input, value, prefixed_name, prefixed_label ):
+        def mapping_callback( input, value, prefixed_name, prefixed_label ):
             if isinstance( value, UnvalidatedValue ):
                 try:
                     return input.to_html_value( value.value, trans.app )
@@ -2623,22 +2639,17 @@ class Tool( object, Dictifiable ):
                 if isinstance(value,list):
                     values = []
                     for val in value:
-                        if isinstance(val, trans.app.model.HistoryDatasetAssociation):
-                            if val.dataset.id in hda_source_dict:
-                                values.append( hda_source_dict[ val.dataset.id ] )
-                            else:
-                                values.append( val )
+                        new_val = map_to_history( val )
+                        if new_val:
+                            values.append( new_val )
+                        else:
+                            values.append( val )
                     return values
-                if isinstance(value, trans.app.model.HistoryDatasetAssociation):
-                    if value.dataset.id in hda_source_dict:
-                        return hda_source_dict[ value.dataset.id ]
-                if isinstance(value, trans.app.model.HistoryDatasetCollectionAssociation):
-                    if value.collection.id in hdca_source_dict:
-                        return hdca_source_dict[ value.collection.id ]
+                else:
+                    return map_to_history( value )
             elif isinstance( input, DataCollectionToolParameter ):
-                if value.collection.id in hdca_source_dict:
-                    return hdca_source_dict[ value.collection.id ]
-        visit_input_values( tool_inputs, params, rerun_callback )
+                return map_to_history( value )
+        visit_input_values( tool_inputs, params, mapping_callback )
 
     def _compare_tool_version( self, trans, job ):
         """