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- package gago
- import (
- "log"
- "math"
- "math/rand"
- "os"
- )
- var (
- ga = GA{
- GenomeFactory: NewVector,
- NPops: 2,
- PopSize: 50,
- Model: ModGenerational{
- Selector: SelTournament{
- NContestants: 3,
- },
- MutRate: 0.5,
- },
- Migrator: MigRing{10},
- MigFrequency: 3,
- Logger: log.New(os.Stdin, "", log.Ldate|log.Ltime),
- }
- nbrGenerations = 5 // Initial number of generations to enhance
- )
- func init() {
- ga.Initialize()
- for i := 0; i < nbrGenerations; i++ {
- ga.Enhance()
- }
- }
- type Vector []float64
- // Implement the Genome interface
- func (X Vector) Evaluate() float64 {
- var sum float64
- for _, x := range X {
- sum += x
- }
- return sum
- }
- func (X Vector) Mutate(rng *rand.Rand) {
- MutNormalFloat64(X, 0.5, rng)
- }
- func (X Vector) Crossover(Y Genome, rng *rand.Rand) (Genome, Genome) {
- var o1, o2 = CrossUniformFloat64(X, Y.(Vector), rng)
- return Vector(o1), Vector(o2)
- }
- func (X Vector) Clone() Genome {
- var XX = make(Vector, len(X))
- copy(XX, X)
- return XX
- }
- func NewVector(rng *rand.Rand) Genome {
- return Vector(InitUnifFloat64(4, -10, 10, rng))
- }
- // Minkowski distance with p = 1
- func l1Distance(x1, x2 Individual) (dist float64) {
- var g1 = x1.Genome.(Vector)
- var g2 = x2.Genome.(Vector)
- for i := range g1 {
- dist += math.Abs(g1[i] - g2[i])
- }
- return
- }
- // Identity model
- type ModIdentity struct{}
- func (mod ModIdentity) Apply(pop *Population) error { return nil }
- func (mod ModIdentity) Validate() error { return nil }
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