func main() { const genes = " abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!." target := "Not all those who wander are lost." calc := func(candidate string) int { return calculate(target, candidate) } start := time.Now() disp := func(candidate string) { fmt.Print(candidate) fmt.Print("\t") fmt.Print(calc(candidate)) fmt.Print("\t") fmt.Println(time.Since(start)) } var solver = new(genetic.Solver) solver.MaxSecondsToRunWithoutImprovement = 1 var best = solver.GetBest(calc, disp, genes, len(target), 1) fmt.Println() fmt.Println(best) fmt.Print("Total time: ") fmt.Println(time.Since(start)) }
func main() { genes := "" for i := 0; i < boardWidthHeight; i++ { genes += strconv.Itoa(i) } start := time.Now() calc := func(candidate string) int { return getFitness(candidate, boardWidthHeight) } disp := func(candidate string) { display(candidate, boardWidthHeight) fmt.Print(candidate) fmt.Print("\t") fmt.Print(getFitness(candidate, boardWidthHeight)) fmt.Print("\t") fmt.Println(time.Since(start)) } var solver = new(genetic.Solver) solver.MaxSecondsToRunWithoutImprovement = 1 var best = solver.GetBest(calc, disp, genes, boardWidthHeight, 2) disp(best) fmt.Print("Total time: ") fmt.Println(time.Since(start)) }
func main() { flag.Parse() if flag.NArg() != 1 { fmt.Println("Usage: go run samples/tsp.go ROUTEFILEPATH") return } var routeFileName = flag.Arg(0) if !File.Exists(routeFileName) { fmt.Println("file " + routeFileName + " does not exist.") return } fmt.Println("using route file: " + routeFileName) idToPointLookup := readPoints(routeFileName) fmt.Println("read " + strconv.Itoa(len(idToPointLookup)) + " points...") calc := func(candidate string) int { return getFitness(candidate, idToPointLookup) } if File.Exists(routeFileName + ".opt.tour") { fmt.Println("found optimal solution file: " + routeFileName + ".opt") optimalRoute := readOptimalRoute(routeFileName+".opt.tour", len(idToPointLookup)) fmt.Println("read " + strconv.Itoa(len(optimalRoute)) + " segments in the optimal route") points := getPointsInOptimalOrder(idToPointLookup, optimalRoute) genes := genericGeneSet[0:len(idToPointLookup)] idToPointLookup = make(map[string]Point, len(idToPointLookup)) for i, v := range points { idToPointLookup[genericGeneSet[i:i+1]] = v } fmt.Print("optimal route: " + genes) fmt.Print("\t") fmt.Println(getFitness(genes, idToPointLookup)) } geneSet := genericGeneSet[0:len(idToPointLookup)] start := time.Now() disp := func(candidate string) { fmt.Print(candidate) fmt.Print("\t") fmt.Print(getFitness(candidate, idToPointLookup)) fmt.Print("\t") fmt.Println(time.Since(start)) } var solver = new(genetic.Solver) solver.MaxSecondsToRunWithoutImprovement = 20 solver.LowerFitnessesAreBetter = true var best = solver.GetBest(calc, disp, geneSet, len(idToPointLookup), 1) fmt.Println() fmt.Println(best, "\t", getFitness(best, idToPointLookup)) fmt.Print("Total time: ") fmt.Println(time.Since(start)) }
func main() { var lengthTable = flag.String("lengthTable", "", "Source length table (2 columns, name<TAB>length)") var targetLength = flag.Int("targetLength", 1000, "Target length for bins") var maxBins = flag.Int("maxBins", 10, "Try and have fewer bins than this") var batchSize = flag.Int("batchSize", 40, "Batch N items at a time. MUST be <90") var slop = flag.Int("slop", 100, "Allow a certain amount of slop.") var patience = flag.Int("patience", 0, "Integer 0-5, with the max being Dalai-Lama-level patience") flag.Parse() resources := []resource{} content, err := ioutil.ReadFile(*lengthTable) if err != nil { //Do something panic(err) } lines := strings.Split(string(content), "\n") for _, line := range lines { data := strings.Split(line, "\t") if len(data) == 2 { length, _ := strconv.Atoi(data[1]) resources = append( resources, *&resource{ name: data[0], length: length, }, ) } } geneSet := "qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM1234567890-=_+!@#$%^&*()<>?|{}[];:',./\\"[0:*batchSize] fmt.Printf("# Round IDX\tBin Idx\tSum\tFeature IDs\n") for i := 0; i <= len(resources) / *batchSize; i++ { min_bound := i * (*batchSize) max_bound := (i + 1) * (*batchSize) max_bound = int(math.Min(float64(max_bound), float64(len(resources)))) localResources := resources[min_bound:max_bound] log.Info(fmt.Sprintf("Processing %d items", len(localResources))) calc := func(candidate string) int { decoded := decodeGenes(candidate, localResources, geneSet) return getFitness(localResources, decoded, *targetLength, *maxBins, *slop) } start := time.Now() disp := func(candidate string) { decoded := decodeGenes(candidate, localResources, geneSet) fitness := getFitness(localResources, decoded, *targetLength, *maxBins, *slop) display(localResources, decoded, fitness, time.Since(start), i, false) } var solver = new(genetic.Solver) solver.MaxSecondsToRunWithoutImprovement = 1 + float64(*patience)*20 solver.MaxRoundsWithoutImprovement = 10 + (*patience)*50 var best = solver.GetBest(calc, disp, geneSet, *maxBins, 32) log.Info("Final:") decoded := decodeGenes(best, localResources, geneSet) fitness := getFitness(localResources, decoded, *targetLength, *maxBins, *slop) display(localResources, decoded, fitness, time.Since(start), i, true) } }