Better defaults

README.md

@@ -54,3 +54,8 @@ weights = [8, 1, 1, 1]
 (for: 1. perturb constant, 2. mutate operator,
 3. append a node, 4. delete a subtree).
 
+
+# TODO
+
+- Record hall of fame
+- Optionally migrate the hall of fame, rather than current bests

eureqa.jl

@@ -322,7 +322,8 @@ function iterate(
     prev = deepcopy(tree)
     
     mutationChoice = rand()
-    weights = [8, 1, 1, 1, 2]
+    weight_for_constant = min(8, countConstants(tree))
+    weights = [weight_for_constant, 1, 1, 1, 2]
     weights /= sum(weights)
     cweights = cumsum(weights)
     n = countNodes(tree)

paralleleureqa.jl

@@ -3,35 +3,36 @@ include("eureqa.jl")
 println("Lets try to learn (x2^2 + cos(x3)) using regularized evolution from scratch")
 const nthreads = Threads.nthreads()
 println("Running with $nthreads threads")
-const npop = 1000
+const npop = 300
 const annealing = true
-const niterations = 100
-const ncyclesperiteration = 30000
+const ncyclesperiteration = 3000
 
-# Generate random initial populations
-allPops = [Population(npop, 3) for j=1:nthreads]
-bestScore = Inf
-# Repeat this many evolutions; we collect and migrate the best
-# each time.
-for k=1:niterations
+function fullRun(niterations::Integer)
+    # Generate random initial populations
+    allPops = [Population(npop, 3) for j=1:nthreads]
+    # Repeat this many evolutions; we collect and migrate the best
+    # each time.
+    for k=1:niterations
 
-    # Spawn threads to run indepdent evolutions, then gather them
-    @inbounds Threads.@threads for i=1:nthreads
-        allPops[i] = run(allPops[i], ncyclesperiteration, annealing, verbose=500)
-    end
+        # Spawn threads to run indepdent evolutions, then gather them
+        @inbounds Threads.@threads for i=1:nthreads
+            allPops[i] = run(allPops[i], ncyclesperiteration, annealing, verbose=500)
+        end
 
-    # Get best 10 models from each evolution. Copy because we re-assign later.
-    bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
-    bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
-    bestCurScore = bestPops.members[bestCurScoreIdx].score
-    println(bestCurScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))
+        # Get best 10 models from each evolution. Copy because we re-assign later.
+        bestPops = deepcopy(Population([member for pop in allPops for member in bestSubPop(pop).members]))
+        bestCurScoreIdx = argmin([bestPops.members[member].score for member=1:bestPops.n])
+        bestCurScore = bestPops.members[bestCurScoreIdx].score
+        println(bestCurScore, " is the score for ", stringTree(bestPops.members[bestCurScoreIdx].tree))
 
-    # Migration
-    for j=1:nthreads
-        for k in rand(1:npop, 50)
-            # Copy in case one gets used twice
-            allPops[j].members[k] = deepcopy(bestPops.members[rand(1:size(bestPops.members)[1])])
+        # Migration
+        for j=1:nthreads
+            for k in rand(1:npop, Integer(npop/2))
+                # Copy in case one gets used twice
+                allPops[j].members[k] = deepcopy(bestPops.members[rand(1:size(bestPops.members)[1])])
+            end
         end
     end
 end
 
+fullRun(10)