Package ec.multiobjective

Class MultiObjectiveFitness

java.lang.Object
ec.Fitness
ec.multiobjective.MultiObjectiveFitness
All Implemented Interfaces:
Prototype, Setup, Serializable, Cloneable, Comparable<Object>
Direct Known Subclasses:
NSGA2MultiObjectiveFitness, SPEA2MultiObjectiveFitness
public class MultiObjectiveFitness extends Fitness
MultiObjectiveFitness is a subclass of Fitness which implements basic multi-objective mechanisms suitable for being used with a variety of multi-objective selection mechanisms, including ones using pareto-optimality.

The object contains two items: an array of floating point values representing the various multiple fitnesses, and a flag (maximize) indicating whether higher is considered better. By default, isIdealFitness() always returns false; you might want to override that, though it'd be unusual -- what is the ideal fitness from the perspective of a pareto front?

The object also contains maximum and minimum fitness values suggested for the problem, on a per-objective basis. By default the maximum values are all 1.0 and the minimum values are all 0.0, but you can change these. Note that maximum does not mean "best" unless maximize is true.

The class also contains utility methods or computing pareto dominance, Pareto Fronts and Pareto Front Ranks, and distance in multiobjective space. The default comparison operators use Pareto Dominance, though this is often overridden by subclasses.

The fitness() method returns the maximum of the fitness values, which is clearly nonsensical: you should not be using this method.

Subclasses of this class may add certain auxiliary fitness measures which are printed out by MultiObjectiveStatistics along with the multiple objectives. To have these values printed out, override the getAuxiliaryFitnessNames() and getAuxiliaryFitnessValues() methods.

Parameters

base.num-objectives
(else)multi.num-objectives
int >= 1		 (the number of fitnesses in the objectives array)
base.maximize
bool = true (default) or false		 (are higher values considered "better"?)
base.maximize.iinvalid input: '<'/i
bool = true (default) or false (are higher values considered "better"?). Overrides the all-objecgive maximization setting. base.max
double (1.0 default) (maximum fitness value for all objectives) base.max.i
double (1.0 default) (maximum fitness value for objective i. Overrides the all-objective maximum fitness.) base.min
double (0.0 (default) (minimum fitness value for all objectives) base.min.i
double = 0.0 (default) (minimum fitness value for objective i. Overrides the all-objective minimum fitness.)

Default Base
multi.fitness

See Also:

  • Field Details

    • MULTI_FITNESS_POSTAMBLE

      public static final String MULTI_FITNESS_POSTAMBLE
      See Also:

    • FITNESS_POSTAMBLE

      public static final String FITNESS_POSTAMBLE
      See Also:

    • P_NUMOBJECTIVES

      public static final String P_NUMOBJECTIVES
      parameter for size of objectives
      See Also:

    • P_MAXOBJECTIVE

      public static final String P_MAXOBJECTIVE
      parameter for max fitness values
      See Also:

    • P_MINOBJECTIVE

      public static final String P_MINOBJECTIVE
      parameter for min fitness values
      See Also:

    • P_MAXIMIZE

      public static final String P_MAXIMIZE
      Is higher better?
      See Also:

    • maxObjective

      public double[] maxObjective
      Desired maximum fitness values. By default these are 1.0. Shared.

    • minObjective

      public double[] minObjective
      Desired minimum fitness values. By default these are 0.0. Shared.

    • maximize

      public boolean[] maximize
      Maximization. Shared.

    • objectives

      protected double[] objectives
      The various fitnesses.

  • Constructor Details

    • MultiObjectiveFitness

      public MultiObjectiveFitness()

  • Method Details

    • getAuxilliaryFitnessNames

      public String[] getAuxilliaryFitnessNames()
      Returns auxilliary fitness value names to be printed by the statistics object. By default, an empty array is returned, but various algorithms may override this to provide additional columns.

    • getAuxilliaryFitnessValues

      public double[] getAuxilliaryFitnessValues()
      Returns auxilliary fitness values to be printed by the statistics object. By default, an empty array is returned, but various algorithms may override this to provide additional columns.

    • isMaximizing

      public boolean isMaximizing()

    • isMaximizing

      public boolean isMaximizing(int objective)

    • getNumObjectives

      public int getNumObjectives()

    • getObjectives

      public double[] getObjectives()
      Returns the objectives as an array. Note that this is the *actual array*. Though you could set values in this array, you should NOT do this -- rather, set them using setObjectives().

    • getObjective

      public double getObjective(int i)

    • setObjectives

      public void setObjectives(EvolutionState state, double[] newObjectives)

    • defaultBase

      public Parameter defaultBase()
      Description copied from interface: Prototype
      Returns the default base for this prototype. This should generally be implemented by building off of the static base() method on the DefaultsForm object for the prototype's package. This should be callable during setup(...).

    • clone

      public Object clone()
      Description copied from interface: Prototype
      Creates a new individual cloned from a prototype, and suitable to begin use in its own evolutionary context.

      Typically this should be a full "deep" clone. However, you may share certain elements with other objects rather than clone hem, depending on the situation:

      • If you hold objects which are shared with other instances, don't clone them.
      • If you hold objects which must be unique, clone them.
      • If you hold objects which were given to you as a gesture of kindness, and aren't owned by you, you probably shouldn't clone them.
      • DON'T attempt to clone: Singletons, Cliques, or Populations, or Subpopulation.
      • Arrays are not cloned automatically; you may need to clone an array if you're not sharing it with other instances. Arrays have the nice feature of being copyable by calling clone() on them.

      Implementations.

      • If no ancestor of yours implements clone(), and you have no need to do clone deeply, and you are abstract, then you should not declare clone().
      • If no ancestor of yours implements clone(), and you have no need to do clone deeply, and you are not abstract, then you should implement it as follows: 

         public Object clone() 
     {
     try
         { 
         return super.clone();
         }
     catch ((CloneNotSupportedException e)
         { throw new InternalError(); } // never happens
     }
      • If no ancestor of yours implements clone(), but you need to deep-clone some things, then you should implement it as follows: 

         public Object clone() 
     {
     try
         { 
         MyObject myobj = (MyObject) (super.clone());

         // put your deep-cloning code here...
         }
     catch ((CloneNotSupportedException e)
         { throw new InternalError(); } // never happens
     return myobj;
     }
      • If an ancestor has implemented clone(), and you also need to deep clone some things, then you should implement it as follows: 

         public Object clone() 
     { 
     MyObject myobj = (MyObject) (super.clone());

     // put your deep-cloning code here...

     return myobj;
     }
      Specified by:
      clone in interface Prototype
      Overrides:
      clone in class Fitness

    • fitness

      public double fitness()
      Returns the Max() of objectives, which adheres to Fitness.java's protocol for this method. Though you should not rely on a selection or statistics method which requires this.
      Specified by:
      fitness in class Fitness

    • setup

      public void setup(EvolutionState state, Parameter base)
      Sets up. This must be called at least once in the prototype before instantiating any fitnesses that will actually be used in evolution.
      Specified by:
      setup in interface Prototype
      Specified by:
      setup in interface Setup
      Overrides:
      setup in class Fitness

    • isIdealFitness

      public boolean isIdealFitness()
      Returns true if this fitness is the "ideal" fitness. Default always returns false. You may want to override this.
      Specified by:
      isIdealFitness in class Fitness

    • equivalentTo

      public boolean equivalentTo(Fitness _fitness)
      Returns true if I'm equivalent in fitness (neither better nor worse) to _fitness. The rule I'm using is this: If one of us is better in one or more criteria, and we are equal in the others, then equivalentTo is false. If each of us is better in one or more criteria each, or we are equal in all criteria, then equivalentTo is true. Multiobjective optimization algorithms may choose to override this to do something else.
      Specified by:
      equivalentTo in class Fitness

    • betterThan

      public boolean betterThan(Fitness fitness)
      Returns true if I'm better than _fitness. The DEFAULT rule I'm using is this: if I am better in one or more criteria, and we are equal in the others, then betterThan is true, else it is false. Multiobjective optimization algorithms may choose to override this to do something else.
      Specified by:
      betterThan in class Fitness

    • paretoDominates

      public boolean paretoDominates(MultiObjectiveFitness other)
      Returns true if I'm better than _fitness. The rule I'm using is this: if I am better in one or more criteria, and we are equal in the others, then betterThan is true, else it is false.

    • partitionIntoParetoFront

      public static ArrayList<Individual> partitionIntoParetoFront(ArrayList<Individual> inds, ArrayList<Individual> front, ArrayList<Individual> nonFront)
      Divides an array of Individuals into the Pareto front and the "nonFront" (everyone else). The Pareto front is returned. You may provide ArrayLists for the front and a nonFront. If you provide null for the front, an ArrayList will be created for you. If you provide null for the nonFront, non-front individuals will not be added to it. This algorithm is O(n^2).

    • getSortedParetoFront

      public static ArrayList<Individual> getSortedParetoFront(ArrayList<Individual> inds)
      Returns the Pareto Front of the provided Individuals, sorted by objective 0, breaking ties with objective 1, and so on...

      This would be useful for printing out statistics.

    • partitionIntoRanks

      public static ArrayList<ArrayList<Individual>> partitionIntoRanks(ArrayList<Individual> inds)
      Divides inds into pareto front ranks (each an ArrayList), and returns them, in order, stored in an ArrayList.

    • getRankings

      public static int[] getRankings(ArrayList<Individual> inds)
      Returns the Pareto rank for each individual. Rank 0 is the best rank, then rank 1, and so on. This is O(n) but it has a high constant overhead because it allocates a hashmap and does some autoboxing.

    • sumSquaredObjectiveDistance

      public double sumSquaredObjectiveDistance(MultiObjectiveFitness other, boolean normalize)
      Returns the sum of the squared difference between two Fitnesses in Objective space.

    • manhattanObjectiveDistance

      public double manhattanObjectiveDistance(MultiObjectiveFitness other)
      Returns the Manhattan difference between two Fitnesses in Objective space.

    • fitnessToString

      public String fitnessToString()
      Description copied from class: Fitness
      Print to a string the fitness in a fashion intended to be parsed in again via readFitness(...). The fitness and evaluated flag should not be included. The default form simply calls toString(), which is almost certainly wrong, and you'll probably want to override this to something else. When overriding, you may wish to check to see if the 'trials' variable is non-null, and issue an error if so.
      Overrides:
      fitnessToString in class Fitness

    • fitnessToStringForHumans

      public String fitnessToStringForHumans()
      Description copied from class: Fitness
      Print to a string the fitness in a fashion readable by humans, and not intended to be parsed in again. The default form simply calls toString(), but you'll probably want to override this to something else.
      Overrides:
      fitnessToStringForHumans in class Fitness

    • readFitness

      public void readFitness(EvolutionState state, LineNumberReader reader) throws IOException
      Description copied from class: Fitness
      Reads in the fitness from a form outputted by fitnessToString() and thus printFitnessForHumans(...). The default version of this method exits the program with an "unimplemented" error.
      Overrides:
      readFitness in class Fitness
      Throws:
      IOException

    • writeFitness

      public void writeFitness(EvolutionState state, DataOutput dataOutput) throws IOException
      Description copied from class: Fitness
      Writes the binary form of an individual out to a DataOutput. This is not for serialization: the object should only write out the data relevant to the object sufficient to rebuild it from a DataInput. The default version exits the program with an "unimplemented" error; you should override this, and be certain to also write the 'trials' variable as well.
      Overrides:
      writeFitness in class Fitness
      Throws:
      IOException

    • readFitness

      public void readFitness(EvolutionState state, DataInput dataInput) throws IOException
      Description copied from class: Fitness
      Reads the binary form of an individual from a DataInput. This is not for serialization: the object should only read in the data written out via printIndividual(state,dataInput). The default version exits the program with an "unimplemented" error; you should override this, and be certain to also write the 'trials' variable as well.
      Overrides:
      readFitness in class Fitness
      Throws:
      IOException

    • setToBestOf

      public void setToBestOf(EvolutionState state, Fitness[] fitnesses)
      Description copied from class: Fitness
      Sets the fitness to be the same value as the best of the provided fitnesses. This method calls setToMeanOf(...), so if that method is unimplemented, this method will also fail.
      Overrides:
      setToBestOf in class Fitness

    • setToMeanOf

      public void setToMeanOf(EvolutionState state, Fitness[] fitnesses)
      Description copied from class: Fitness
      Sets the fitness to be the same value as the mean of the provided fitnesses. The default version of this method exits with an "unimplemented" error; you should override this.
      Overrides:
      setToMeanOf in class Fitness

    • setToMedianOf

      public void setToMedianOf(EvolutionState state, Fitness[] fitnesses)
      Description copied from class: Fitness
      Sets the fitness to be the median of the provided fitnesses. This method calls setToMeanOf(...), so if that method is unimplemented, this method will also fail.
      Overrides:
      setToMedianOf in class Fitness