object NSGA3
NSGA-III algorithm for many-objective problems
Deb, K., & Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE transactions on evolutionary computation, 18(4), 577-601.
For U-NSGA-III, see Seada, H., & Deb, K. (2015, March). U-NSGA-III: a unified evolutionary optimization procedure for single, multiple, and many objectives: proof-of-principle results. In International conference on evolutionary multi-criterion optimization (pp. 34-49). Springer, Cham.
- Companion
- class
Type members
Classlikes
case
class Result[P](continuous: Vector[Double], discrete: Vector[Int], fitness: Vector[Double], individual: Individual[P])
Types
Value members
Concrete methods
def adaptiveBreeding[S, P](operatorExploration: Double, discrete: Vector[D], fitness: P => Vector[Double], reject: Option[Genome => Boolean], lambda: Int): (S, Individual[P]) => Genome
def elitism[S, P](mu: Int, references: ReferencePoints, components: Vector[C], fitness: P => Vector[Double]): S => Individual[P]
def expression[P](express: (Vector[Double], Vector[Int]) => P, components: Vector[C]): Genome => Individual[P]
def initialGenomes(populationSize: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome => Boolean], rng: Random): Vector[Genome]
def result[P](population: Vector[Individual[P]], continuous: Vector[C], fitness: P => Vector[Double], keepAll: Boolean): Vector[Result[P]]
def result(nsga3: NSGA3, population: Vector[Individual[Vector[Double]]]): Vector[Result[Vector[Double]]]