setup_test.go 1.5 KB

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  1. package gago
  2. import (
  3. "log"
  4. "math"
  5. "math/rand"
  6. "os"
  7. )
  8. var (
  9. ga = GA{
  10. GenomeFactory: NewVector,
  11. NPops: 2,
  12. PopSize: 50,
  13. Model: ModGenerational{
  14. Selector: SelTournament{
  15. NContestants: 3,
  16. },
  17. MutRate: 0.5,
  18. },
  19. Migrator: MigRing{10},
  20. MigFrequency: 3,
  21. Logger: log.New(os.Stdin, "", log.Ldate|log.Ltime),
  22. }
  23. nbrGenerations = 5 // Initial number of generations to enhance
  24. )
  25. func init() {
  26. ga.Initialize()
  27. for i := 0; i < nbrGenerations; i++ {
  28. ga.Enhance()
  29. }
  30. }
  31. type Vector []float64
  32. // Implement the Genome interface
  33. func (X Vector) Evaluate() float64 {
  34. var sum float64
  35. for _, x := range X {
  36. sum += x
  37. }
  38. return sum
  39. }
  40. func (X Vector) Mutate(rng *rand.Rand) {
  41. MutNormalFloat64(X, 0.5, rng)
  42. }
  43. func (X Vector) Crossover(Y Genome, rng *rand.Rand) (Genome, Genome) {
  44. var o1, o2 = CrossUniformFloat64(X, Y.(Vector), rng)
  45. return Vector(o1), Vector(o2)
  46. }
  47. func (X Vector) Clone() Genome {
  48. var XX = make(Vector, len(X))
  49. copy(XX, X)
  50. return XX
  51. }
  52. func NewVector(rng *rand.Rand) Genome {
  53. return Vector(InitUnifFloat64(4, -10, 10, rng))
  54. }
  55. // Minkowski distance with p = 1
  56. func l1Distance(x1, x2 Individual) (dist float64) {
  57. var g1 = x1.Genome.(Vector)
  58. var g2 = x2.Genome.(Vector)
  59. for i := range g1 {
  60. dist += math.Abs(g1[i] - g2[i])
  61. }
  62. return
  63. }
  64. // Identity model
  65. type ModIdentity struct{}
  66. func (mod ModIdentity) Apply(pop *Population) error { return nil }
  67. func (mod ModIdentity) Validate() error { return nil }