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Merging old MK branch rev. 214: adding 4 multimodal problems

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This commit is contained in:
Marcel Kronfeld
2010-02-24 10:46:14 +00:00
parent 4fd8546424
commit 5cda401398
4 changed files with 261 additions and 0 deletions
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55
src/eva2/server/go/problems/F16Problem.java Normal file
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package eva2.server.go.problems;
public class F16Problem extends AbstractProblemDouble implements InterfaceMultimodalProblem, Interface2DBorderProblem {
int dim = 10;
public F16Problem() {
dim=10;
}
public F16Problem(F16Problem other) {
dim = other.dim;
}
@Override
public double[] eval(double[] x) {
double[] res = new double[1];
double sum = 0;
for (int i=0; i<getProblemDimension(); i++) {
sum += Math.sin(10*Math.log(x[i]));
}
res[0] = 1.-((1./getProblemDimension())*sum);
return res;
}
@Override
public int getProblemDimension() {
return dim;
}
public void setProblemDimension(int newDim) {
dim = newDim;
}
@Override
public Object clone() {
return new F16Problem(this);
}
public double getRangeLowerBound(int n) {
return 0.25;
}
public double getRangeUpperBound(int n) {
return 10.;
}
public String getName() {
return "Vincent function";
}
public String globalInfo() {
return "Multiple optima with increasing densitiy near the lower bounds, there for decreasing attractor size.";
}
}

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src/eva2/server/go/problems/F17Problem.java Normal file
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package eva2.server.go.problems;
public class F17Problem extends AbstractProblemDouble implements
InterfaceMultimodalProblem {
int dim = 10;
public F17Problem() {
dim=10;
}
public F17Problem(F17Problem other) {
dim=other.dim;
}
public double[] eval(double[] x) {
double[] res = new double[1];
double sum = 0;
for (int i=0; i<getProblemDimension()-1; i++) {
sum += x[i]*x[i]+2.*(x[i+1]*x[i+1]);
sum += 0.7 - 0.3*Math.cos(3*Math.PI*x[i]) - 0.4*Math.cos(4*Math.PI*x[i+1]);
}
res[0] = sum;
return res;
}
public int getProblemDimension() {
return dim;
}
public void setProblemDimension(int newDim) {
dim = newDim;
}
public Object clone() {
return new F17Problem(this);
}
public String getName() {
return "F17-Problem";
}
public String globalInfo() {
return "Bohachevsky function, numerous optima on an oval hyperparabola with similar attractor sizes but decreasing fitness towards the bounds.";
}
}

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src/eva2/server/go/problems/F18Problem.java Normal file
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package eva2.server.go.problems;
public class F18Problem extends AbstractProblemDouble implements
InterfaceMultimodalProblem {
int dim = 10;
double alpha = 1.;
public F18Problem() {
dim=10;
}
public F18Problem(F18Problem other) {
dim=other.dim;
}
public double[] eval(double[] x) {
double[] res = new double[1];
double sum = 0;
for (int i=0; i<getProblemDimension(); i++) {
sum += Math.pow(Math.sin(5*Math.PI*x[i]), alpha);
}
res[0] = 1.-sum/getProblemDimension();
return res;
}
public double getRangeLowerBound(int n) {
return 0.;
}
public double getRangeUpperBound(int n) {
return 1.;
}
public int getProblemDimension() {
return dim;
}
public void setProblemDimension(int newDim) {
dim = newDim;
}
public Object clone() {
return new F18Problem(this);
}
public String getName() {
return "F18-Problem";
}
public String globalInfo() {
return "N-Function from Shir&Baeck, PPSN 2006.";
}
/**
* @return the alpha
*/
public double getAlpha() {
return alpha;
}
/**
* @param alpha the alpha to set
*/
public void setAlpha(double alpha) {
this.alpha = alpha;
}
}

94
src/eva2/server/go/problems/F19Problem.java Normal file
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package eva2.server.go.problems;
import java.util.Arrays;
import wsi.ra.math.RNG;
public class F19Problem extends AbstractProblemDouble implements
InterfaceMultimodalProblem {
int dim = 10;
transient private double[] alphas, As;
transient private int[] A,B;
public F19Problem() {
alphas=null;
dim=10;
setDefaultRange(Math.PI);
}
public F19Problem(F19Problem other) {
dim=other.dim;
alphas=null;
}
public void initProblem() {
super.initProblem();
if (alphas==null) {
// create static random data
alphas = RNG.randomVector(dim, -Math.PI, Math.PI);
A = RNG.randomVector(dim*dim, -100, 100);
B = RNG.randomVector(dim*dim, -100, 100);
As = transform(alphas);
}
}
private double[] transform(double[] x) {
double[] v = new double[dim];
Arrays.fill(v, 0.);
for (int i=0; i<dim; i++) {
for (int j=0; j<dim; j++) {
v[i] += get(A, i, j)*Math.sin(x[j])+get(B, i, j)*Math.cos(x[j]);
}
}
return v;
}
/**
* Get a value in row i, col j, from matrix M (represented as vector).
* @param M
* @param i
* @param j
* @return
*/
private int get(int[] M, int i, int j) {
return M[i*dim+j];
}
public double[] eval(double[] x) {
double[] res = new double[1];
double[] Bs = transform(x);
double sum=0;
for (int i=0; i<getProblemDimension(); i++) {
sum += Math.pow(As[i]-Bs[i], 2);
}
res[0] = sum;
return res;
}
public int getProblemDimension() {
return dim;
}
public void setProblemDimension(int newDim) {
dim = newDim;
if (alphas!=null && (newDim>alphas.length)) { // only recreate if really necessary
alphas=null;
A=null;
B=null;
}
}
public Object clone() {
return new F19Problem(this);
}
public String getName() {
return "F19-Problem";
}
public String globalInfo() {
return "Fletcher-Powell function with up to 2^n optima from Shir&Baeck, PPSN 2006, after Bäck 1996. Alphas and Matrices A and B are randomly created but kept when the dimension is decreased.";
}
}