func TestElitePool(t *testing.T) {
pop := dummies()
pool := sel.ElitePool(5, 10)
for i := range pop {
pool.Put(pop[i])
}
for i := dummy(9); 4 < i; i-- {
if pool.Get().(dummy) != i {
t.Fail()
return
}
}
}
bounds = 30 // Bounds of object parameters.
precision = 1e-16 // Desired precision.
)
// Global objects
var (
// Count of the number of fitness evaluations.
count struct {
sync.Mutex
n int
}
// Each of the 40 members of the population generates 7 children and adds
// them to this pool. This pool returns to each member a different one of
// most fit to be their replacement in the next generation.
selector = sel.ElitePool(40, 280)
// A free-list used to recycle memory.
vectors = sync.Pool{
New: func() interface{} {
return make(real.Vector, dim)
},
}
)
// The ackley type specifies our genome. We evolve a real-valued vector that
// optimizes the ackley function. Each genome also contains a vector of strategy
// parameters used with a self-adaptive evolution strategy.
type ackley struct {
gene real.Vector // the object vector to optimize
steps real.Vector // strategy parameters for mutation
