// 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
}
// Evolution implements the body of the evolution loop.
func Evolution(q evo.Genome, suitors []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.
mom := q.(*queens)
dad := sel.BinaryTournament(suitors...).(*queens)
child := &queens{gene: make([]int, len(mom.gene))}
perm.PMX(child.gene, mom.gene, dad.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 < 5; 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
}
