Genetic Algorithm and Particle Swarm Optimization written in Go
Given f(x,y) = cos(x^2 * y^2) * 1/(x^2 * y^2 + 1)
Find (x,y) such as f(x,y) reaches its maximum
Answer f(0,0) = 1
package main
import (
"fmt"
"math"
"math/rand"
"github.com/khezen/evoli"
)
// 3d cosine that gets smaller as you move away from 0,0
func f(x, y float64) float64 {
d := x*x + y*y
return math.Cos(d) * (1 / (d/10 + 1))
}
type FIndividual struct {
v []float64
x []float64
fitness float64
}
func (i *FIndividual) Equal(other evoli.Individual) bool {
return i == other
}
func (i *FIndividual) Fitness() float64 {
return i.fitness
}
func (i *FIndividual) SetFitness(newFitness float64) {
i.fitness = newFitness
}
type FPositioner struct {
}
func (p *FPositioner) Position(indiv, pBest, gBest evoli.Individual, c1, c2 float64) (evoli.Individual, error) {
fIndiv, ok1 := indiv.(*FIndividual)
fPBest, ok2 := pBest.(*FIndividual)
fGBest, ok3 := gBest.(*FIndividual)
if !ok1 || !ok2 || !ok3 {
return nil, fmt.Errorf("invalid individual type")
}
newIndiv := FIndividual{
v: make([]float64, len(fIndiv.v)),
x: make([]float64, len(fIndiv.v)),
}
w := 0.9
for d := range fIndiv.v {
rp := rand.Float64()
rg := rand.Float64()
newIndiv.v[d] = w*fIndiv.v[d] +
c1*rp*(fPBest.x[d]-fIndiv.x[d]) +
c2*rg*(fGBest.x[d]-fIndiv.x[d])
newIndiv.x[d] = fIndiv.x[d] + newIndiv.v[d]
}
return &newIndiv, nil
}
type FEvaluater struct {
}
func (e *FEvaluater) Evaluate(indiv evoli.Individual) (Fitness float64, err error) {
fIndiv, ok := indiv.(*FIndividual)
if !ok {
return 0, fmt.Errorf("invalid individual type")
}
return f(fIndiv.x[0], fIndiv.x[1]), nil
}
func main() {
pop := evoli.NewPopulation(50)
for i := 0; i < pop.Cap(); i++ {
x := rand.Float64()*20 - 10
y := rand.Float64()*20 - 10
vx := rand.Float64()*20 - 10
vy := rand.Float64()*20 - 10
pop.Add(&FIndividual{
x: []float64{x, y},
v: []float64{vx, vy},
})
}
positioner := &FPositioner{}
evaluator := &FEvaluater{}
sw := evoli.NewSwarm(pop, positioner, .2, .2, evaluator)
for i := 0; i < 100; i++ {
err := sw.Next()
if err != nil {
panic(err)
}
}
// evaluate the latest population
for _, v := range sw.Population().Slice() {
f, err := evaluator.Evaluate(v)
if err != nil {
panic(err)
}
v.SetFitness(f)
}
fmt.Printf("Max Value: %.2f\n", sw.Alpha().Fitness())
}Max Value: 1.00package main
import (
"fmt"
"math"
"math/rand"
"github.com/khezen/evoli"
)
// 3d cosine that gets smaller as you move away from 0,0
func h(x, y float64) float64 {
d := x*x + y*y
return math.Cos(d) * (1 / (d/10 + 1))
}
type HIndividual struct {
v []float64
x []float64
fitness float64
}
func (i *HIndividual) Equal(other evoli.Individual) bool {
return i == other
}
func (i *HIndividual) Fitness() float64 {
return i.fitness
}
func (i *HIndividual) SetFitness(newFitness float64) {
i.fitness = newFitness
}
type HMutater struct {
}
func (m HMutater) Mutate(indiv evoli.Individual) (evoli.Individual, error) {
x := rand.Float64()*20 - 10
y := rand.Float64()*20 - 10
vx := rand.Float64()*20 - 10
vy := rand.Float64()*20 - 10
return &FIndividual{
x: []float64{x, y},
v: []float64{vx, vy},
}, nil
}
type HCrosser struct {
}
func (h HCrosser) Cross(indiv1, indiv2 evoli.Individual) (evoli.Individual, error) {
fIndiv1, _ := indiv1.(*FIndividual)
fIndiv2, _ := indiv2.(*FIndividual)
return &FIndividual{
x: []float64{(fIndiv1.x[0] + fIndiv2.x[0]) / 2, (fIndiv1.x[1] + fIndiv2.x[1]) / 2},
v: []float64{(fIndiv1.v[0] + fIndiv2.v[0]) / 2, (fIndiv1.v[1] + fIndiv2.v[1]) / 2},
}, nil
}
type HEvaluater struct {
}
func (e HEvaluater) Evaluate(indiv evoli.Individual) (Fitness float64, err error) {
fIndiv, ok := indiv.(*FIndividual)
if !ok {
return 0, fmt.Errorf("invalid individual type")
}
return f(fIndiv.x[0], fIndiv.x[1]), nil
}
func main() {
pop := evoli.NewPopulation(50)
for i := 0; i < pop.Cap(); i++ {
x := rand.Float64()*20 - 10
y := rand.Float64()*20 - 10
vx := rand.Float64()*20 - 10
vy := rand.Float64()*20 - 10
pop.Add(&FIndividual{
x: []float64{x, y},
v: []float64{vx, vy},
})
}
crosser := HCrosser{}
mutater := HMutater{}
evaluator := HEvaluater{}
mutationProbability := .02
selecter := evoli.NewTruncationSelecter()
survivorSize := 30
ga := evoli.NewGenetic(pop, selecter, survivorSize, crosser, mutater, mutationProbability, evaluator)
for i := 0; i < 100; i++ {
err := ga.Next()
if err != nil {
panic(err)
}
}
// evaluate the latest population
for _, v := range ga.Population().Slice() {
f, err := evaluator.Evaluate(v)
if err != nil {
panic(err)
}
v.SetFitness(f)
}
fmt.Printf("Max Value: %.2f\n", ga.Alpha().Fitness())
}Max Value: 1.00If you have any problems or questions, please ask for help through a GitHub issue.
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