func Example_gp() {
// create initial population
gp.SetSeed(1)
pset := gp.CreatePrimSet(1, "x")
pset.Add(num.Add, num.Sub, num.Mul, num.Div, num.Neg, num.V(0), num.V(1))
generator := gp.GenFull(pset, 1, 3)
pop, evals := gp.CreatePopulation(500, generator).Evaluate(eval{}, 1)
best := pop.Best()
fmt.Printf("gen=%d evals=%d fit=%.4f\n", 0, evals, best.Fitness)
// setup genetic variations
tournament := gp.Tournament(3)
mutate := gp.MutUniform(gp.GenGrow(pset, 0, 2))
crossover := gp.CxOnePoint()
// loop till reach target fitness or exceed no. of generations
for gen := 1; gen <= 40 && best.Fitness < 1; gen++ {
offspring := tournament.Select(pop, len(pop))
pop, evals = gp.VarAnd(offspring, crossover, mutate, 0.5, 0.2).Evaluate(eval{}, 1)
best = pop.Best()
fmt.Printf("gen=%d evals=%d fit=%.4f\n", gen, evals, best.Fitness)
}
fmt.Println(best.Code.Format())
// Output:
// set random seed: 1
// gen=0 evals=500 fit=0.1203
// gen=1 evals=299 fit=0.3299
// gen=2 evals=286 fit=0.6633
// gen=3 evals=265 fit=0.6633
// gen=4 evals=280 fit=0.6633
// gen=5 evals=291 fit=0.6633
// gen=6 evals=302 fit=0.6633
// gen=7 evals=294 fit=1.0000
// (x + (((x / 1) - ((x / 1) * -(((x * x) + x)))) * (1 * x)))
}
func getStats(t *testing.T, gen int) *Stats {
pset := gp.CreatePrimSet(1, "x")
pset.Add(num.Add, num.Sub, num.Mul, num.Div, num.Neg, num.V(0), num.V(1))
pop := gp.CreatePopulation(1000, gp.GenFull(pset, 1, 3))
for i := range pop {
pop[i].Fitness = rand.Float64()
}
s := Create(pop, gen, len(pop))
t.Log(s)
return s
}
