Class MaF5

All Implemented Interfaces:
AutoCloseable, Problem, AnalyticalProblem

public class MaF5 extends DTLZ implements AnalyticalProblem
The MaF5 test problem, which is a convex and badly scaled version of the DTLZ4 problem. This problem exhibits the following properties:
  • Convex Pareto front
  • Badly scaled decision variables
  • Biased
  • Constructor Details

    • MaF5

      public MaF5(int numberOfObjectives)
      Constructs an MaF5 test problem with the specified number of objectives.
      Parameters:
      numberOfObjectives - the number of objectives for this problem
  • Method Details

    • evaluate

      public void evaluate(Solution solution)
      Description copied from interface: Problem
      Evaluates the solution, updating the solution's objectives in place. Algorithms must explicitly call this method when appropriate to evaluate new solutions or reevaluate modified solutions.
      Specified by:
      evaluate in interface Problem
      Parameters:
      solution - the solution to be evaluated
    • generate

      public Solution generate()
      Description copied from interface: AnalyticalProblem
      Returns a randomly-generated solution using the analytical solution to this problem. The exact behavior of this method depends on the implementation, but in general (1) the solutions should be non-dominated and (2) spread uniformly across the Pareto front.

      It is not always possible to guarantee these conditions. For example, a discontinuous / disconnected Pareto surface could generate dominated solutions, and a biased problem could result in non-uniform distributions. Therefore, we recommend callers filter solutions through a NondominatedPopulation, in particular one that maintains a spread of solutions.

      Furthermore, some implementations may not provide the corresponding decision variables for the solution. These implementations should indicate this by returning a solution with 0 decision variables.

      Specified by:
      generate in interface AnalyticalProblem
      Returns:
      a randomly-generated Pareto optimal solution to this problem