diff --git a/caliGraph.py b/caliGraph.py
index b227a2a..6526929 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -545,8 +545,8 @@ def addScoreToLabels(G):
             if 'rating' in node and node['rating'] != None:
                 node['label'] += " ("+str(node['rating'])+")"
             else:
-                if 'score' in node and node['score'] != None:
-                    node['label'] += " (~{:.2f}±{:.1f})".format(node['score'], node['se'] if 'se' in node else 0.0)
+                if 'score' in node and node['score'] != None and 'se' in node:
+                    node['label'] += " (~{:.2f}±{:.1f})".format(node['score'], node['se'])
                 else:
                     node['label'] += " (~0±∞)"
 
@@ -775,7 +775,7 @@ def waveFlow(G, node, n, dist, menge, firstEdge=False):
         if node in bestlist or node in keeplist:
             waveFlow(G, node, m, dist, menge, firstEdge=firstEdge)
 
-def evaluateFitness(books):
+def evaluateFitness(books, debugPrint=False):
     global weights
     G = buildBookGraph(books)
     graphAddAuthors(G, books)
@@ -808,8 +808,9 @@ def evaluateFitness(books):
             # reward seperation linearly
             linSepLoss.append(abs(score - mu))
     regressionLoss = sum([(1-w)**2 for w in weights.values()])
-    print(sum(errSq)/len(errSq), 0.005*regressionLoss, 0.2*boundsLoss/len(ratedBooks), 0.5*sum(linSepLoss)/len(linSepLoss))
-    return sum(errSq)/len(errSq) + 0.005*regressionLoss + 0.2*boundsLoss/len(ratedBooks) - 0.5*sum(linSepLoss)/len(linSepLoss)
+    if debugPrint:
+        print(sum(errSq)/len(errSq), 0.005*regressionLoss, 0.2*boundsLoss/len(ratedBooks), 1.0*sum(linSepLoss)/len(linSepLoss))
+    return sum(errSq)/len(errSq) + 0.005*regressionLoss + 0.2*boundsLoss/len(ratedBooks) - 1.0*sum(linSepLoss)/len(linSepLoss)
 
 def train(gamma = 1):
     global weights