org.apache.commons.math3.optimization.ConvergenceChecker Java Examples

The following examples show how to use org.apache.commons.math3.optimization.ConvergenceChecker. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: Cardumen_00257_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #2
Source File: PowellOptimizer.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * This constructor allows to specify a user-defined convergence checker,
 * in addition to the parameters that control the default convergence
 * checking procedure and the line search tolerances.
 *
 * @param rel Relative threshold.
 * @param abs Absolute threshold.
 * @param checker Convergence checker.
 * @throws NotStrictlyPositiveException if {@code abs <= 0}.
 * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
 */
public PowellOptimizer(double rel,
                       double abs,
                       ConvergenceChecker<PointValuePair> checker) {
    super(checker);

    if (rel < MIN_RELATIVE_TOLERANCE) {
        throw new NumberIsTooSmallException(rel, MIN_RELATIVE_TOLERANCE, true);
    }
    if (abs <= 0) {
        throw new NotStrictlyPositiveException(abs);
    }
    relativeThreshold = rel;
    absoluteThreshold = abs;

    // Line search tolerances can be much less stringent than the tolerances
    // required for the optimizer itself.
    line = new LineSearch(FastMath.sqrt(rel),
                          FastMath.sqrt(abs));
}
Example #3
Source File: 1_CMAESOptimizer.java    From SimFix with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #4
Source File: Arja_00177_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #5
Source File: Arja_0055_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #6
Source File: Arja_0028_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #7
Source File: CMAESOptimizer.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #8
Source File: Arja_00140_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #9
Source File: Arja_00166_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #10
Source File: Arja_0052_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #11
Source File: Arja_00104_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #12
Source File: Cardumen_00105_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #13
Source File: Cardumen_0037_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #14
Source File: Cardumen_00161_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #15
Source File: Cardumen_00257_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #16
Source File: 1_CMAESOptimizer.java    From SimFix with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #17
Source File: Cardumen_00213_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #18
Source File: JGenProg2017_0020_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #19
Source File: JGenProg2017_00131_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #20
Source File: JGenProg2017_0059_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #21
Source File: JGenProg2017_0059_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #22
Source File: JGenProg2017_0089_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #23
Source File: JGenProg2017_00111_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #24
Source File: jKali_0035_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #25
Source File: CMAESOptimizer.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 *
 * @since 3.1
 */
public CMAESOptimizer(int maxIterations,
                      double stopFitness,
                      boolean isActiveCMA,
                      int diagonalOnly,
                      int checkFeasableCount,
                      RandomGenerator random,
                      boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #26
Source File: Math_18_CMAESOptimizer_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #27
Source File: Math_19_CMAESOptimizer_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #28
Source File: Math_19_CMAESOptimizer_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #29
Source File: Math_20_CMAESOptimizer_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
Example #30
Source File: CMAESOptimizer_t.java    From coming with MIT License 6 votes vote down vote up 
/**
 * @param lambda Population size.
 * @param inputSigma Initial search volume; sigma of offspring objective variables.
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 */
public CMAESOptimizer(int lambda, double[] inputSigma,
                      int maxIterations, double stopFitness,
                      boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
                      RandomGenerator random, boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.lambda = lambda;
    this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}