no more parallel

Author: jinyeom

config.go

@@ -55,8 +55,7 @@ type Config struct {
 	CoeffUnmatching   float64 `json:"coeffUnmatching"`   // unmatching genes
 	CoeffMatching     float64 `json:"coeffMatching"`     // matching genes
 
-	// Miscellaneous settings
-	Lamarckian      bool     `json:"lamarckian"`      // Lamarckian evolution
+	// CPPN settings
 	CPPNActivations []string `json:"cppnActivations"` // additional activations
 }
 
@@ -112,8 +111,7 @@ func (c *Config) Summarize() {
 	fmt.Fprintf(w, "+ Unmatching connection genes\t%.3f\t\n", c.CoeffUnmatching)
 	fmt.Fprintf(w, "+ Matching connection genes\t%.3f\t\n\n", c.CoeffMatching)
 
-	fmt.Fprintf(w, "Miscellaneous settings\t\n")
-	fmt.Fprintf(w, "+ Lamarckian evolution\t%t\t\n", c.Lamarckian)
+	fmt.Fprintf(w, "CPPN settings\t\n")
 	fmt.Fprintf(w, "+ CPPN Activation functions\t%s\t\n", c.CPPNActivations)
 
 	w.Flush()

config_pole_balancing.json

@@ -4,7 +4,7 @@
 	"numInputs": 4,
 	"numOutputs": 2,
 	"fullyConnected": false,
-	"numGenerations": 50,
+	"numGenerations": 30,
 	"populationSize": 100,
 	"initFitness": 0.0,
 	"minimizeFitness": false,

config_template.json

@@ -9,7 +9,6 @@
 	"minimizeFitness": false,
 	"survivalRate": 0.0,
 	"stagnationLimit": 0,
-	"hallOfFameSize": 0,
 	"ratePerturb": 0.0,
 	"rateAddNode": 0.0,
 	"rateAddConn": 0.0,

config_xor.json

@@ -3,6 +3,7 @@
 	"verbose": true,
 	"numInputs": 3,
 	"numOutputs": 1,
+	"fullyConnected": true,
 	"numGenerations": 50,
 	"populationSize": 50,
 	"initFitness": 9999.0,

genome.go

@@ -193,27 +193,13 @@ func (g *Genome) String() string {
 // Evaluate takes an evaluation function and evaluates its fitness. Only perform
 // the evaluation if it hasn't yet. If the lamarckian indicator is true, encode
 // the phenotype neural network back into the genome.
-func (g *Genome) Evaluate(evaluate EvaluationFunc, lamarckian bool) {
+func (g *Genome) Evaluate(evaluate EvaluationFunc) {
 	if g.evaluated {
 		return
 	}
 	nn := NewNeuralNetwork(g)
 	g.Fitness = evaluate(nn)
 	g.evaluated = true
-
-	// for Lamarckian evolution, any changes to the neural network's weights are
-	// encoded back to the genome's connection genes.
-	if lamarckian {
-		for _, to := range nn.Neurons {
-			for from, weight := range to.Synapses {
-				for _, conn := range g.ConnGenes {
-					if from.ID == conn.From && to.ID == conn.To {
-						conn.Weight = weight
-					}
-				}
-			}
-		}
-	}
 }
 
 // ExportJSON exports a JSON file that contains this genome's information. If

neat.go

@@ -22,7 +22,6 @@ import (
 	"math"
 	"math/rand"
 	"sort"
-	"sync"
 )
 
 // NEAT is the implementation of NeuroEvolution of Augmenting Topology (NEAT).
@@ -122,17 +121,9 @@ func (n *NEAT) Summarize(gen int) {
 // Evaluate evaluates fitness of every genome in the population. After the
 // evaluation, their fitness scores are recored in each genome.
 func (n *NEAT) Evaluate() {
-	var wg sync.WaitGroup
-
-	for i := range n.Population {
-		wg.Add(1)
-		go func(j int) {
-			defer wg.Done()
-			n.Population[j].Evaluate(n.Evaluation, n.Config.Lamarckian)
-		}(i)
+	for _, genome := range n.Population {
+		genome.Evaluate(n.Evaluation)
 	}
-
-	wg.Wait()
 }
 
 // Speciate performs speciation of each genome. The speciation mechanism is as