Package org.moeaframework.problem.WFG
Class WFG4
java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.WFG.WFG
org.moeaframework.problem.WFG.WFG4
- All Implemented Interfaces:
- AutoCloseable,- Named,- AnalyticalProblem,- Problem
The WFG4 test problem.
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Field SummaryFields inherited from class org.moeaframework.problem.AbstractProblemnumberOfConstraints, numberOfObjectives, numberOfVariables
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Constructor SummaryConstructors
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Method SummaryMethods inherited from class org.moeaframework.problem.WFG.WFGgetName, newSolutionMethods inherited from class org.moeaframework.problem.AbstractProblemclose, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariablesMethods inherited from class java.lang.Objectclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitMethods inherited from interface org.moeaframework.problem.Problemclose, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, isType
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Constructor Details- 
WFG4public WFG4(int M) Constructs a WFG4 problem instance with the specified number of objectives.- Parameters:
- M- the number of objectives for this problem
 
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WFG4public WFG4(int k, int l, int M) Constructs a WFG4 problem instance with the specified number of position-related and distance-related variables and the specified number of objectives.- Parameters:
- k- the number of position-related variables for this problem
- l- the number of distance-related variables for this problem
- M- the number of objectives for this problem
 
 
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Method Details- 
evaluateDescription copied from interface:ProblemEvaluates 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.- Parameters:
- solution- the solution to be evaluated
 
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generateDescription copied from interface:AnalyticalProblemReturns 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 0decision variables.- Returns:
- a randomly-generated Pareto optimal solution to this problem
 
 
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