func TestAckley(t *testing.T) {
fmt.Printf("Minimize the Ackley function with n=%d\n", dim)
// Setup:
// We initialize a set of 40 random solutions,
// then add them to a generational population.
seed := make([]evo.Genome, 40)
for i := range seed {
seed[i] = &ackley{
gene: real.Random(dim, 30),
steps: real.Random(dim, 1),
}
}
var pop gen.Population
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: %8.3g | Mean: %8.3g | Min: %8.3g | RSD: %9.2e",
n,
-stats.Min(),
-stats.Mean(),
-stats.Max(),
-stats.RSD())
return false
})
// Terminate after 200,000 fitness evaluations.
pop.Poll(0, func() bool {
count.Lock()
n := count.n
count.Unlock()
return n > 200000
})
// Terminate if the standard deviation is low.
pop.Poll(0, func() bool {
stats := pop.Stats()
return stats.SD() < precision
})
pop.Wait()
selector.Close()
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("\nSolution:", best)
}
func TestQueens(t *testing.T) {
fmt.Printf("Find a solution to %d-queens\n", dim)
// Setup:
// We create an initial set of random candidates and divide them into "islands".
// Each island is evolved independently in a generational population.
// The islands are then linked together into a graph population with
seed := make([]evo.Genome, size)
for i := range seed {
seed[i] = &queens{gene: perm.New(dim)}
}
islands := make([]evo.Genome, isl)
islSize := size / isl
for i := range islands {
var island gen.Population
island.Evolve(seed[i*islSize:(i+1)*islSize], Evolution)
islands[i] = &island
}
pop := graph.Ring(isl)
pop.Evolve(islands, gen.Migrate(migration, delay))
// 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 xterm escape code to clear the line
// Because this is a minimization problem, the fitness is negative.
// Thus we update the statistics accordingly.
fmt.Printf("\x1b[2K\rCount: %7d | Max: %3.0f | Mean: %3.0f | Min: %3.0f | RSD: %9.2e",
n,
-stats.Min(),
-stats.Mean(),
-stats.Max(),
-stats.RSD())
return false
})
// Terminate when we've found the solution (when max is 0)
pop.Poll(0, func() bool {
stats := pop.Stats()
return stats.Max() == 0
})
// Terminate if We've converged to a deviation is less than 0.01
pop.Poll(0, func() bool {
stats := pop.Stats()
return stats.SD() < 1e-2
})
// 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("\nSolution:", best)
}