func main() {
rand.Seed(time.Now().UTC().UnixNano())
m := ga.NewMultiMutator()
msh := new(ga.GAShiftMutator)
msw := new(ga.GASwitchMutator)
m.Add(msh)
m.Add(msw)
param := ga.GAParameter{
Initializer: new(ga.GARandomInitializer),
Selector: ga.NewGATournamentSelector(0.7, 5),
Breeder: new(ga.GA2PointBreeder),
Mutator: m,
PMutate: 0.1,
PBreed: 0.7}
gao := ga.NewGA(param)
genome := ga.NewOrderedIntGenome([]int{10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0}, score)
gao.Init(100, genome) //Total population
gao.OptimizeUntil(func(best ga.GAGenome) bool {
return best.Score() <= 680
})
gao.PrintTop(10)
fmt.Printf("Calls to score = %d\n", scores)
fmt.Printf("%s\n", m.Stats())
}
func main() {
rand.Seed(time.Now().UTC().UnixNano())
m := ga.NewMultiMutator()
msh := new(ga.GAShiftMutator)
msw := new(ga.GASwitchMutator)
m.Add(msh)
m.Add(msw)
param := ga.GAParameter{
Initializer: new(ga.GARandomInitializer),
Selector: ga.NewGATournamentSelector(0.7, 5),
Breeder: new(ga.GA2PointBreeder),
Mutator: m,
PMutate: 0.2,
PBreed: 0.2}
gao := ga.NewGA(param)
genome := ga.NewFixedBitstringGenome(make([]bool, len(theset)), score)
gao.Init(50, genome) //Total population
for {
gao.Optimize(1) // Run genetic algorithm for 20 generations.
best := gao.Best().(*ga.GAFixedBitstringGenome)
sum := 0
if best.Score() == 0 {
for n, value := range best.Gene {
if value {
fmt.Printf("%d,", theset[n])
sum += theset[n]
}
}
fmt.Printf(" = %d / %f\n", sum, best.Score())
break
}
}
fmt.Printf("Calls to score = %d\n", scores)
}