// Evolve implements the inner loop of the evolutionary algorithm.
// The population calls the Evolve method of each genome, in parallel. Then,
// each receiver returns a value to replace it in the next generation.
func (q *queens) Evolve(matingPool ...evo.Genome) evo.Genome {
// Crossover:
// We're implementing a diffusion model. For each member of the population,
// we receive a small mating pool containing only our neighbors. We choose
// a mate using a random binary tournament and create a child with
// partially mapped crossover.
mate := sel.BinaryTournament(matingPool...).(*queens)
child := &queens{gene: pool.Get().([]int)}
perm.PMX(child.gene, q.gene, mate.gene)
// Mutation:
// Perform n random swaps where n is taken from an exponential distribution.
mutationCount := math.Ceil(rand.ExpFloat64() - 0.5)
for i := float64(0); i < mutationCount; i++ {
j := rand.Intn(len(child.gene))
k := rand.Intn(len(child.gene))
child.gene[j], child.gene[k] = child.gene[k], child.gene[j]
}
// Replacement:
// Only replace if the child is better or equal.
if q.Fitness() > child.Fitness() {
return q
}
return child
}
func TestPMX(t *testing.T) {
mom := rand.Perm(8)
dad := rand.Perm(8)
child := make([]int, 8)
perm.PMX(child, mom, dad)
validate(t, child)
}
