Package org.moeaframework.problem.misc
Class Schaffer
java.lang.Object
org.moeaframework.problem.AbstractProblem
org.moeaframework.problem.misc.Schaffer
- All Implemented Interfaces:
AutoCloseable
,Problem
,AnalyticalProblem
The Schaffer problem. The Schaffer problem is univariate, with the optimum defined by
0 <= x <= 2
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Properties:
- Connected Pareto set
- Convex Pareto front
References:
- Schaffer, J. D. (1984). "Some Experiments in Machine Learning using Vector Evaluated Genetic Algorithms." Ph.D. Thesis, Vanderbilt University, Nashville, USA.
- Schaffer, J. D. (1985). "Multiple Objective Optimization with Vector Evaluated Genetic Algorithms." Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93-100.
- Van Veldhuizen, D. A (1999). "Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations." Air Force Institute of Technology, Ph.D. Thesis, Appendix B.
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Field Summary
Fields inherited from class org.moeaframework.problem.AbstractProblem
numberOfConstraints, numberOfObjectives, numberOfVariables
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionvoid
Evaluates the solution, updating the solution's objectives in place.generate()
Returns a randomly-generated solution using the analytical solution to this problem.Returns a new solution for this problem.Methods inherited from class org.moeaframework.problem.AbstractProblem
close, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.moeaframework.core.Problem
close, getName, getNumberOfConstraints, getNumberOfObjectives, getNumberOfVariables, isType
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Constructor Details
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Schaffer
public Schaffer()Constructs the Schaffer problem.
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Method Details
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evaluate
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. -
newSolution
Description copied from interface:Problem
Returns a new solution for this problem. Implementations must initialize the variables so that the valid range of values is defined, but typically leave the actual value at a default or undefined state.- Specified by:
newSolution
in interfaceProblem
- Returns:
- a new solution for this problem
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generate
Description copied from interface:AnalyticalProblem
Returns a randomly-generated solution using the analytical solution to this problem. Note however that discontinuous Pareto surfaces may result in some solutions generated by this method being dominated by other generated solutions. It is therefore recommended using aNondominatedPopulation
to remove dominated solutions prior to using the generated reference set.The generated solutions should be spread uniformly across the entire Pareto frontier; however, this is a suggestion and is not a requirement of this interface.
- Specified by:
generate
in interfaceAnalyticalProblem
- Returns:
- a randomly-generated Pareto optimal solution to this problem
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