MGO is a scala library based on the cake pattern for multi-objective evolutionary / genetic algorithms:
- enforcing immutability,
- exposes a modular and extensible architecture,
- implements state of the art algorithms,
- take advantage of multi-core architectures.
MGO implements NGSAII, SMSEMOEA, CMAES and other diversity based evolutionary algorithms.
MGO is licenced under the GNU Affero GPLv3 software licence.
Define a problem, for instance ZDT4:
import fr.iscpif.mgo._ import math._ import util.Random trait ZDT4 extends GAProblem with MGFitness { def min = Seq.fill(genomeSize)(0.0) def max = 1.0 :: Seq.fill(genomeSize - 1)(5.0) type P = Seq[Double] override def express(g: Seq[Double], rng: Random) = Seq(f1(g), f2(g)) override def evaluate(p: P, rng: Random) = p def f1(x: Seq[Double]) = x(0) def f2(x: Seq[Double]) = g(x) * (1 - sqrt(x(0) / g(x))) def g(x: Seq[Double]) = 1 + 10 * (genomeSize - 1) + (1 until genomeSize).map { i => pow(x(i), 2) - 10 * cos(4 * Pi * x(i)) }.sum }Define the optimisation algorithm, for instance NSGAII:
val m = new ZDT4 with NSGAII with CounterTermination { def steps = 1000 def mu = 200 def lambda = 200 def genomeSize = 10 }Run the optimisation:
implicit val rng = newRNG(42) val res = m.evolve.untilConverged { s => println(s.generation) } val output = Resource.fromFile("/tmp/res.csv") for { r <- res.population.toIndividuals } { def line = m.scale(m.values.get(r.genome)) ++ m.fitness(r) output.append(line.mkString(",") + "\n") }For more examples, have a look at the main/scala/fr/iscpif/mgo/test directory in the repository.
libraryDependencies += "fr.iscpif" %% "mgo" % "version"