Evolutionary Algorithm implemented in Go
$ go get github.com/tsurubee/eago
The following example code attempts to minimize the Ackley function using GA.
The Ackley function have a minimum value of 0 when variables(x1 and x2) is equal to 0.
package main
import (
"github.com/tsurubee/eago"
"log"
"math"
)
type Variables []float64
func (V Variables) Initialization() eago.Genome {
return Variables(eago.InitFloatVector(2, 32, -32))
}
func (V Variables) Fitness() float64 {
return Ackley(V)
}
func (V Variables) Mutation() {
eago.AddNormalFloat(V, 0.5)
}
func (V Variables) Crossover(X eago.Genome) eago.Genome {
return Variables(eago.BLXalpha(V, X.(Variables), 0.3))
}
func Ackley(X []float64) float64 {
a, b, c, d := 20.0, 0.2, 2*math.Pi, float64(len(X))
var s1, s2 float64
for _, x := range X {
s1 += x * x
s2 += math.Cos(c * x)
}
return -a*math.Exp(-b*math.Sqrt(s1/d)) - math.Exp(s2/d) + a + math.Exp(1)
}
func main() {
var v Variables
ga := eago.NewGA(eago.GAConfig{
PopulationSize: 30,
NGenerations: 20,
CrossoverRate: 0.8,
MutationRate: 0.01,
})
if err := ga.Minimize(v); err != nil {
log.Fatal(err)
}
}If the above sample code is named as main.go and executed as follows, the result of parameter search is displayed on the standard output.
$ go run main.go
Generation 1: Fitness=11.208 Solution=[-4.501 0.087]
Generation 2: Fitness=10.001 Solution=[-3.592 -0.833]
Generation 3: Fitness=9.165 Solution=[-3.841 -0.960]
Generation 4: Fitness=5.747 Solution=[-1.426 -0.235]
Generation 5: Fitness=4.258 Solution=[-0.824 0.380]
Generation 6: Fitness=2.797 Solution=[-0.843 -0.011]
Generation 7: Fitness=2.605 Solution=[-0.955 -0.030]
Generation 8: Fitness=2.605 Solution=[-0.955 -0.030]
Generation 9: Fitness=2.604 Solution=[-0.925 0.016]
Generation 10: Fitness=2.604 Solution=[-0.925 0.016]
Generation 11: Fitness=2.479 Solution=[-0.346 -0.054]
Generation 12: Fitness=1.216 Solution=[-0.184 -0.006]
Generation 13: Fitness=0.615 Solution=[-0.111 -0.001]
Generation 14: Fitness=0.501 Solution=[-0.096 -0.007]
Generation 15: Fitness=0.267 Solution=[-0.055 0.024]
Generation 16: Fitness=0.056 Solution=[0.010 -0.014]
Generation 17: Fitness=0.056 Solution=[0.010 -0.014]
Generation 18: Fitness=0.053 Solution=[-0.005 -0.015]
Generation 19: Fitness=0.031 Solution=[0.010 0.003]
Generation 20: Fitness=0.013 Solution=[-0.000 0.004]package main
import (
"github.com/tsurubee/eago"
"log"
)
func objectiveFunc(x []float64) float64 {
return x[0] * x[0] + x[1] * x[1]
}
func main() {
pso := eago.NewDefaultPSO()
pso.NParticle = 5
pso.NStep = 20
pso.Min = -20
pso.Max = 10
if err := pso.Minimize(objectiveFunc, 2); err != nil {
log.Fatal(err)
}
}If the above sample code is named as main.go and executed as follows, the result of parameter search is displayed on the standard output.
$ go run main.go
Step 0: Fitness=8.124 Position=[-2.168 1.851]
Step 1: Fitness=8.124 Position=[-2.168 1.851]
Step 2: Fitness=7.394 Position=[-1.618 2.186]
Step 3: Fitness=6.958 Position=[1.068 2.412]
Step 4: Fitness=6.958 Position=[1.068 2.412]
Step 5: Fitness=4.809 Position=[-2.106 0.611]
Step 6: Fitness=2.215 Position=[-1.052 1.053]
Step 7: Fitness=1.535 Position=[0.580 1.095]
Step 8: Fitness=0.163 Position=[0.023 0.403]
Step 9: Fitness=0.163 Position=[0.023 0.403]
Step 10: Fitness=0.055 Position=[-0.175 0.156]
Step 11: Fitness=0.055 Position=[-0.175 0.156]
Step 12: Fitness=0.055 Position=[-0.175 0.156]
Step 13: Fitness=0.055 Position=[-0.175 0.156]
Step 14: Fitness=0.055 Position=[-0.175 0.156]
Step 15: Fitness=0.055 Position=[-0.175 0.156]
Step 16: Fitness=0.055 Position=[-0.180 0.151]
Step 17: Fitness=0.044 Position=[0.019 0.210]
Step 18: Fitness=0.013 Position=[-0.111 0.023]
Step 19: Fitness=0.008 Position=[-0.083 -0.031]-
Genetic algorithm
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Particle swarm optimization