// 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 Evolve(current evo.Genome, matingPool []evo.Genome) evo.Genome {
// Selection:
// Select each parent using a simple random binary tournament
mom := sel.BinaryTournament(matingPool...).(*tsp)
dad := sel.BinaryTournament(matingPool...).(*tsp)
// Crossover:
// Edge recombination
child := &tsp{gene: pool.Get().([]int)}
perm.EdgeX(child.gene, mom.gene, dad.gene)
// Mutation:
// There is an n% chance for the gene to have n random swaps
// and an n% chance to undergo n steps of a greedy 2-opt hillclimber
for rand.Float64() < 0.1 {
perm.RandSwap(child.gene)
}
for rand.Float64() < 0.1 {
child.TwoOpt()
}
// Replacement:
// Only replace if the child is better or equal
if current.Fitness() > child.Fitness() {
return current
}
return child
}
// 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 (g *Genome) Evolve(suitors ...evo.Genome) evo.Genome {
// Creation:
child := &Genome{}
// Crossover:
mom := sel.BinaryTournament(suitors...).(*Genome)
dad := sel.BinaryTournament(suitors...).(*Genome)
child.UniformX(&mom.Siam, &dad.Siam)
// Mutation:
for chance := rand.Float64(); chance < 0.2; chance = rand.Float64() {
child.Mutate()
}
// Replacement:
return child
}
func TestBinaryTournament(t *testing.T) {
var stats evo.Stats
pop := dummies()
for i := 0; i < 1e6; i++ {
winner := sel.BinaryTournament(pop...).(dummy)
stats = stats.Put(float64(winner))
}
if stats.Mean() < 5.5 || 6.5 < stats.Mean() {
t.Fail()
}
}
