Go語言實(shí)現(xiàn)遺傳算法的實(shí)例代碼

package main
import "fmt"
func main() {
  fmt.Println("Hello, World!")
}

$ go run hello.go 
Hello, World!

type GeneticAlgorithmSettings struct {
 PopulationSize int
 MutationRate int
 CrossoverRate int
 NumGenerations int
 KeepBestAcrossPopulation bool
}
type GeneticAlgorithmRunner interface {
 GenerateInitialPopulation(populationSize int) []interface{}
 PerformCrossover(individual1, individual2 interface{}, mutationRate int) interface{}
 PerformMutation(individual interface{}) interface{}
 Sort([]interface{})
}

type QuadraticGA struct {}
func (l QuadraticGA) GenerateInitialPopulation(populationSize int) []interface{}{
 initialPopulation := make([]interface{}, 0, populationSize)
 for i:= 0; i  populationSize; i++ {
  initialPopulation = append(initialPopulation, makeNewEntry())
 }
 return initialPopulation
}
func (l QuadraticGA) PerformCrossover(result1, result2 interface{}, _ int) interface{}{
 return (result1.(float64) + result2.(float64)) / 2
}
func (l QuadraticGA) PerformMutation(_ interface{}, _ int) interface{}{
 return makeNewEntry()
}
func (l QuadraticGA) Sort(population []interface{}){
 sort.Slice(population, func(i, j int) bool {
  return calculate(population[i].(float64)) > calculate(population[j].(float64))
 })
}

settings := ga.GeneticAlgorithmSettings{
  PopulationSize: 5,
  MutationRate: 10,
  CrossoverRate: 100,
  NumGenerations: 20,
  KeepBestAcrossPopulation: true,
}
best, err := ga.Run(QuadraticGA{}, settings)
if err != nil {
  println(err)
}else{
  fmt.Printf("Best: x: %f y: %f\n", best, calculate(best.(float64)))
}

func makeNewEntry() float64 {
  return highRange * rand.Float64()
}
func calculate(x float64) float64 {
  return math.Pow(x, 2) - 6*x + 2 // minimum should be at x=3
}

func Run(geneticAlgoRunner GeneticAlgorithmRunner, settings GeneticAlgorithmSettings) (interface{}, error){
  population := geneticAlgoRunner.GenerateInitialPopulation(settings.PopulationSize)
  geneticAlgoRunner.Sort(population)
  bestSoFar := population[len(population) - 1]
  for i:= 0; i  settings.NumGenerations; i++ {
   newPopulation := make([]interface{}, 0, settings.PopulationSize)
   if settings.KeepBestAcrossPopulation {
     newPopulation = append(newPopulation, bestSoFar)
   }
   // perform crossovers with random selection
   probabilisticListOfPerformers := createStochasticProbableListOfIndividuals(population)
   newPopIndex := 0
   if settings.KeepBestAcrossPopulation{
     newPopIndex = 1
   }
   for ; newPopIndex  settings.PopulationSize; newPopIndex++ {
     indexSelection1 := rand.Int() % len(probabilisticListOfPerformers)
     indexSelection2 := rand.Int() % len(probabilisticListOfPerformers)
     // crossover
     newIndividual := geneticAlgoRunner.PerformCrossover(
      probabilisticListOfPerformers[indexSelection1],
      probabilisticListOfPerformers[indexSelection2], settings.CrossoverRate)
     // mutate
     if rand.Intn(101)  settings.MutationRate {
      newIndividual = geneticAlgoRunner.PerformMutation(newIndividual)
     }
     newPopulation = append(newPopulation, newIndividual)
   }
   population = newPopulation
   // sort by performance
   geneticAlgoRunner.Sort(population)
   // keep the best so far
   bestSoFar = population[len(population) - 1]
  }
  return bestSoFar, nil
}
func createStochasticProbableListOfIndividuals(population []interface{}) []interface{} {
  totalCount, populationLength:= 0, len(population)
  for j:= 0; j  populationLength; j++ {
   totalCount += j
  }
  probableIndividuals := make([]interface{}, 0, totalCount)
  for index, individual := range population {
   for i:= 0; i  index; i++{
     probableIndividuals = append(probableIndividuals, individual)
   }
  }
  return probableIndividuals
}

type Quad3D struct {
  x, y float64
}
func makeNewQuadEntry(newX, newY float64) Quad3D {
  return Quad3D{
   x: newX,
   y: newY,
  }
}
func calculate3D(entry Quad3D) float64 {
  return math.Pow(entry.x, 2)- 6 * entry.x + math.Pow(entry.y, 2)- 6 * entry.y + 2
}
type Quadratic3dGA struct {
}
func (l Quadratic3dGA) GenerateInitialPopulation(populationSize int)[]interface{}{
  initialPopulation := make([]interface{}, 0, populationSize)
  for i:= 0; i  populationSize; i++ { initialPopulation = append(initialPopulation, makeNewQuadEntry(makeNewEntry(), makeNewEntry())) } return initialPopulation } func (l Quadratic3dGA) PerformCrossover(result1, result2 interface{}, mutationRate int) interface{}{ r1Entry, r2Entry := result1.(Quad3D), result2.(Quad3D) return makeNewQuadEntry((r1Entry.x + r2Entry.x) / 2, (r1Entry.y + r2Entry.y) / 2,) } func (l Quadratic3dGA) PerformMutation(_ interface{}) interface{}{ return makeNewQuadEntry(makeNewEntry(), makeNewEntry()) } func (l Quadratic3dGA) Sort(population []interface{}){ sort.Slice(population, func(i, j int) bool { return calculate3D(population[i].(Quad3D)) > calculate3D(population[j].(Quad3D))
  })
}
func quadratic3dMain(){
  settings := ga.GeneticAlgorithmSettings{
   PopulationSize: 25,
   MutationRate: 10,
   CrossoverRate: 100,
   NumGenerations: 20,
   KeepBestAcrossPopulation: true,
  }
  best, err := ga.Run(Quadratic3dGA{}, settings)
  entry := best.(Quad3D)
  if err != nil {
   println(err)
  }else{
   fmt.Printf("Best: x: %f y: %f z: %f\n", entry.x, entry.y, calculate3D(entry))
  }
}

func main() {
  beforeQuadTime := time.Now()
  quadraticMain()
  afterQuadTime := time.Since(beforeQuadTime)
  fmt.Printf("%d\n", afterQuadTime)
  before3dQuadTime := time.Now()
  quadratic3dMain()
  after3dQuatTime := time.Since(before3dQuadTime)
  fmt.Printf("%d\n", after3dQuatTime)
}

Best: x: 3.072833 y: -6.994695
136,876
Best: x: 3.891671 y: 4.554884 z: -12.787259
4,142,778

Best: 121.409960:, $58100
QB: Aaron Rodgers - 23.777778
RB: Latavius Murray - 15.228571
RB: DeMarco Murray - 19.980000
WR: Kelvin Benjamin - 11.800000
WR: Stefon Diggs - 14.312500
WR: Alshon Jeffery - 9.888889
TE: Connor Hamlett - 8.200000
D: Philadelphia Eagles - 10.777778
K: Phil Dawson - 7.444444
16,010,182