diff --git a/caliGraph.py b/caliGraph.py
index 8d4440a..ef9f96a 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -634,12 +634,12 @@ def buildFullGraph(darkMode=False):
     graphAddTopLists(G, books, darkMode=darkMode)
     graphAddSeries(G, books, darkMode=darkMode)
     graphAddTags(G, books, darkMode=darkMode)
-    runPagerank(G)
     return G, books
 
 
 def genScores(G, books):
     globMu, globStd = calcRecDist(G, books)
+    runPagerank(G)
     scoreOpinions(G, globMu, globStd)
     scoreUnread(G, globMu, globStd)
     return globMu, globStd
@@ -708,16 +708,16 @@ def recommendNBooks(G, mu, std, n, removeTopListsB=True, removeUselessRecommende
     removeBad(G, mu+std/2, groups=['recommender'])
     removeKeepBest(G, int(n*2) + 5, maxDistForRead=2)
     removeEdge(G)
-    removeHighSpanTags(G, 10)
-    removeHighSpanReadBooks(G, 10)
+    removeHighSpanTags(G, 8)
+    removeHighSpanReadBooks(G, 14)
     removeDangling(G, alsoBooks=False)
-    pruneRecommenders(G, 13)
-    pruneTags(G, 13)
+    pruneRecommenders(G, 16)
+    pruneTags(G, 9)
     removeBad(G, mu, groups=['book'])
     removeUselessReadBooks(G)
-    pruneTags(G, 12)
-    pruneAuthorCons(G, int(n/5))
-    pruneRecommenders(G, 12 - min(5, n/20))
+    pruneTags(G, 8)
+    pruneAuthorCons(G, int(n/5)+3)
+    pruneRecommenders(G, 16 - min(4, n/20))
     removeUselessSeries(G, mu)
     removeUselessTags(G)
     if removeTopListsB:
@@ -911,7 +911,6 @@ def evaluateFitness(books, debugPrint=False):
     graphAddTopLists(G, books)
     graphAddSeries(G, books)
     graphAddTags(G, books)
-    runPagerank(G)
 
     ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None]
     boundsLoss = 0
@@ -946,8 +945,8 @@ def evaluateFitness(books, debugPrint=False):
     for g in gradient:
         gradient[g] /= len(errSq)
     if debugPrint:
-        print(sum(errSq)/len(errSq), 0.003*regressionLoss, 0.2*boundsLoss/len(ratedBooks), 1.0*sum(linSepLoss)/len(linSepLoss))
-    fit = sum(errSq)/len(errSq) + 0.003*regressionLoss + 0.2*boundsLoss/len(ratedBooks) - 1.0*sum(linSepLoss)/len(linSepLoss)
+        print(sum(errSq)/len(errSq), 0.001*regressionLoss, 0.1*boundsLoss/len(ratedBooks), 0.5*sum(linSepLoss)/len(linSepLoss))
+    fit = sum(errSq)/len(errSq) + 0.001*regressionLoss + 0.1*boundsLoss/len(ratedBooks) - 0.5*sum(linSepLoss)/len(linSepLoss)
     return fit, gradient
 
 def train(initGamma, full=True):
@@ -992,7 +991,7 @@ def train(initGamma, full=True):
                 gamma = initGamma
                 if random.random() < 0.50:
                     for wt in weights:
-                        weights[wt] = random.random()
+                        weights[wt] = random.random()*2-0.5
                 else:
                     weights = copy.copy(bestWeights)
                     for wt in weights: