public class GoalAttainmentDomination extends Object implements DominationStrategy
DominationStrategy. The dominance relation is
based on the goal attainment approach described in Carlos M. Fonseca
and Peter J. Fleming: Genetic Algorithms for Multiobjective Optimization:
Formulation, Discussion and Generalization. In: Genetic Algorithms:
Proceedings of the Fifth International Conference, pp 416-423.
Morgan Kaufmann, 1993.
ParetoDomination and uses its array based
method ParetoDomination.dominates(double[], double[]).
SatisfactionConstraints on Objectives! If no
satisfaction for an Objective o exists, the constraint is consequently
o < negative infinity or o > positive infinity". Semantically, a
satisfaction constraint x < a will mean, that the attribute x satisfies
the goal, if its value v(x) is less than a.DominationStrategy,
SatisfactionConstraint,
ParetoDomination| Constructor and Description |
|---|
GoalAttainmentDomination(ParetoDomination paretoDomination) |
| Modifier and Type | Method and Description |
|---|---|
boolean |
dominates(Objectives o1,
Objectives o2)
Checks two
Objectives for domination. |
boolean |
weaklyDominates(Objectives o1,
Objectives o2)
Checks two
Objectives for weak domination. |
@Inject public GoalAttainmentDomination(ParetoDomination paretoDomination)
public boolean dominates(Objectives o1, Objectives o2)
Objectives for domination. The dominance relation is Goal-Attainment-Domination.SatisfactionConstraints are present, this method just passes
the parameters on to its internal strategy for Pareto Domination.dominates in interface DominationStrategyo1 - the objectives to checko2 - the objectives to compare withtrue, if o1 dominates o2false, otherwisepublic boolean weaklyDominates(Objectives o1, Objectives o2)
Objectives for weak domination. The dominance relation is Goal-Attainment-Domination.weaklyDominates in interface DominationStrategyo1 - the objectives to checko2 - the objectives to compare withtrue, if o1 weakly dominates o2false, otherwise