func TestTSP(t *testing.T) {
fmt.Println("Minimize tour of US capitals - optimal is", best)
// Setup:
// We create a random initial population
// and evolve it using a generational model.
seed := make([]evo.Genome, size)
for i := range seed {
seed[i] = &tsp{gene: pool.Get().([]int)}
}
pop = graph.Hypercube(size)
pop.Evolve(seed, Evolve)
// Continuously print statistics while the optimization runs.
pop.Poll(0, func() bool {
count.Lock()
n := count.n
count.Unlock()
stats := pop.Stats()
// "\x1b[2K" is the escape code to clear the line
// The fitness of minimization problems is negative
fmt.Printf("\x1b[2K\rCount: %7d | Max: %6.0f | Mean: %6.0f | Min: %6.0f | RSD: %7.2e",
n,
-stats.Min(),
-stats.Mean(),
-stats.Max(),
-stats.RSD())
return false
})
// Stop when we get close. Finding the true minimum could take a while.
pop.Poll(0, func() bool {
stats := pop.Stats()
return -stats.Max() < best*1.1
})
// Terminate after 2,000,000 fitness evaluations.
pop.Poll(0, func() bool {
count.Lock()
n := count.n
count.Unlock()
return n > 2e6
})
pop.Wait()
best := seed[0]
bestFit := seed[0].Fitness()
for i := range seed {
fit := seed[i].Fitness()
if fit > bestFit {
best = seed[i]
bestFit = fit
}
}
fmt.Println("\nTour:", best)
}
func TestTSP(t *testing.T) {
fmt.Println("Minimize tour of US capitals - optimal is", best)
// Setup:
// We create a random initial population
// and evolve it using a generational model.
init := make([]evo.Genome, size)
for i := range init {
init[i] = &tsp{gene: pool.Get().([]int)}
}
pop = graph.Hypercube(init)
pop.Start()
// Tear-down:
// Upon returning, we cleanup our resources and print the solution.
defer func() {
pop.Close()
fmt.Println("\nTour:", evo.Max(pop))
}()
// Run:
// We continuously poll the population for statistics used in the
// termination conditions.
for {
count.Lock()
n := count.n
count.Unlock()
stats := pop.Stats()
// "\x1b[2K" is the escape code to clear the line
// The fitness of minimization problems is negative
fmt.Printf("\x1b[2K\rCount: %7d | Max: %6.0f | Mean: %6.0f | Min: %6.0f | RSD: %7.2e",
n,
-stats.Min(),
-stats.Mean(),
-stats.Max(),
stats.RSD())
// Stop when we get close. Finding the true minimum could take a while.
if -stats.Max() < best*1.1 {
return
}
// Force stop after some number fitness evaluations
if n > stop {
t.Fail()
return
}
}
}
