Mise a jour des exemples

examples/ant/ant.ez

@@ -640,7 +640,7 @@
     Reduce parents operator: Tournament 4 
     Surviving parents: 50%
     Reduce offspring operator: Tournament 2
-    Surviving offspring: 96%
+    Surviving offspring: 50%
     Final reduce operator: Tournament 2
     Elitism: Weak
 

examples/michalewicz/michalewicz.ez

@@ -176,6 +176,7 @@ CPPFLAGS+=
 
   Remote island model: false
   IP file: ip.txt 			//File containing all the remote island's IP
+  Server port:2929
   Migration probability : 1.0
 
   Save population: false

examples/rastrigin/rastrigin.ez

@@ -73,9 +73,6 @@ float gauss()
 \end
 
 \After everything else function:
-  ofstream SaveFile("save.csv", ios::app);
-  SaveFile << bBest->fitness << ";" << endl;
-  SaveFile.close();
 \end
 
 \At the beginning of each generation function:
@@ -172,6 +169,7 @@ CPPFLAGS+=
 
   Remote island model: false
   IP file: ip.txt 			//File containing all the remote island's IP
+  Server port: 2929
   Migration probability : 0.8
 
   Save population: false

examples/regression/regression.ez

- /*_________________________________________________________
+ 
+
+/*_________________________________________________________
 
   This is a standard GP implementation on EASEA, 
   aimed for regression.
@@ -10,7 +12,7 @@
 __________________________________________________________*/
 
 \User declarations :
-
+#define PI (3.141592653589793)
 \end
 
 \User functions:
@@ -27,17 +29,17 @@ __________________________________________________________*/
 int generateData(float*** inputs, float** outputs){
   int i=0;
 
-  (*inputs) = new float*[gNO_FITNESS_CASES];
-  (*outputs) = new float[gNO_FITNESS_CASES];
+  (*inputs) = new float*[NO_FITNESS_CASES];
+  (*outputs) = new float[NO_FITNESS_CASES];
   
-  for( i=0 ; i<gNO_FITNESS_CASES ; i++ ){
+  for( i=0 ; i<NO_FITNESS_CASES ; i++ ){
     (*inputs)[i]=new float[VAR_LEN];
     float x = random(-10.,+10.);
     (*inputs)[i][0] = x;
     (*outputs)[i] = POLY(x);
   }
 
-  return gNO_FITNESS_CASES;
+  return NO_FITNESS_CASES;
 }
 
 
@@ -60,7 +62,6 @@ void free_data(){
 \After everything else function:
 {
   toDotFile( ((IndividualImpl*)EA->population->Best)->root, "best", 0);
-  toString(((IndividualImpl*)EA->population->Best)->root);
   free_data();
 }
 \end
@@ -89,9 +90,7 @@ GenomeClass {
 }
 \end
 
-\GenomeClass::display:{
-  cout << *this << endl;
-}	
+\GenomeClass::display:
 \end
 
 \GenomeClass::initialiser :
@@ -137,11 +136,12 @@ ERROR = powf(expected_value-EVOLVED_VALUE,2);
 \end
 
 \GenomeClass::evaluator accumulator :
-return sqrtf(ERROR/gNO_FITNESS_CASES);
+return sqrtf(ERROR/NO_FITNESS_CASES);
 \end
 
 \User Makefile options: 
-CXXFLAGS+=#-I/usr/local/cuda/common/inc/ -I/usr/local/cuda/include/
+
+CXXFLAGS+=-I/usr/local/cuda/common/inc/ -I/usr/local/cuda/include/
 LDFLAGS+=
 \end
 
@@ -177,5 +177,5 @@ LDFLAGS+=
   nb of GPUs : 1
   size of prog buffer : 20000000
 
-  nb of fitness cases : 4096
+  nb of fitness cases : 128
 \end

examples/sphere/sphere.ez

@@ -69,10 +69,6 @@ float gauss()
 
 \After everything else function:
   //cout << "After everything else function called" << endl;
-  cout << "Best individual " << bBest->fitness << endl;
-  ofstream SaveFile("save.csv", ios::app);
-  SaveFile << bBest->fitness << ";" << endl;
-  SaveFile.close();
 \end
 
 \At the beginning of each generation function:
@@ -169,6 +165,7 @@ CPPFLAGS+=
 
   Remote island model: false
   IP file: ip.txt 			//File containing all the remote island's IP
+  Server port:2929
   Migration probability : 1.0
 
   Save population: false

examples/weierstrass/ip.txt

+127.0.0.1:2929
+127.0.0.1:2930

examples/weierstrass_std/weierstrass.ez → examples/weierstrass/weierstrass.ez

@@ -48,7 +48,7 @@ __device__ __host__ inline float Weierstrass(float x[SIZE], int n)  // Weierstra
    float res = 0.;
    float val[SIZE];
    float b=2.;
-   float h = 0.25;
+   float h = 0.35;
 
    for (int i = 0;i<n; i++) {
 	val[i] = 0.;
@@ -164,14 +164,13 @@ return NbMut;
 \end
 
 \User Makefile options: 
-CPPFLAGS+=
 \end
 
 \Default run parameters :        // Please let the parameters appear in this order
-  Number of generations : 100     	// NB_GEN
+  Number of generations : 100   	// NB_GEN
   Time limit: 0 			// In seconds, 0 to deactivate
-  Population size : 100			//POP_SIZE
-  Offspring size : 100 // 40% 
+  Population size : 2048			//POP_SIZE
+  Offspring size : 2048 // 40% 
   Mutation probability : 1       // MUT_PROB
   Crossover probability : 1      // XOVER_PROB
   Evaluator goal : minimise      // Maximise
@@ -186,15 +185,16 @@ CPPFLAGS+=
   Elite: 1
   Print stats: true				//Default: 1
   Generate csv stats file:false			
-  Generate gnuplot script:true
-  Generate R script:true
-  Plot stats:false				//Default: 0
+  Generate gnuplot script:false
+  Generate R script:false
+  Plot stats:true				//Default: 0
 
   Remote island model: true
   IP file: ip.txt 			//File containing all the remote island's IP
-  Migration probability: 0.1
+  Server port : 2929
+  Migration probability: 0.33
 
-  Save population: true
+  Save population: false
   Start from file:false
 \end
 	

examples/weierstrass_gpu/weierstrass.ez

-/*_________________________________________________________
-
-Test functions
-log normal adaptive mutation
-Selection operator: Tournament
-__________________________________________________________*/
-
-\User declarations :
-#define SIZE 100
-#define X_MIN -1.
-#define X_MAX 1.
-#define ITER 120      
-#define Abs(x) ((x) < 0 ? -(x) : (x))
-#define MAX(x,y) ((x)>(y)?(x):(y))
-#define MIN(x,y) ((x)<(y)?(x):(y))
-#define SIGMA  1.                     /*  mutation parameter */
-#define PI 3.141592654
-
- 
-float pMutPerGene=0.1;
-
-struct gpuOptions;
-
-\end
-
-\User functions:
-//fitness function
-#include <math.h>
-
-__device__ __host__ inline static float SQR(float d)
-{
-  return (d*d);
-}
-
-__device__ __host__ inline float rosenbrock( float const *x)
-{
-  float qualitaet;
-  int i;
-  int DIM = SIZE;
-        qualitaet = 0.0;
-
-        for( i = DIM-2; i >= 0; --i)
-          qualitaet += 100.*SQR(SQR(x[i])-x[i+1]) + SQR(1.-x[i]);
-        return ( qualitaet);
-} /* f_rosenbrock() */
-
-__device__ __host__ inline float Weierstrass(float *x, int n)  // Weierstrass multimidmensionnel h = 0.25
-{
-   float res = 0.;
-   float val[SIZE];
-   float b=2.;
-   float h = 0.25;
-
-   for (int i = 0;i<n; i++) {
-	val[i] = 0.;
-    	for (int k=0;k<ITER;k++)
-		val[i] += pow(b,-(float)k*h) * sin(pow(b,(float)k)*x[i]);
-	res += Abs(val[i]);
-	}
-   return (res);
-} 
-
-float gauss()
-/* Generates a normally distributed random value with variance 1 and 0 mean.
-    Algorithm based on "gasdev" from Numerical recipes' pg. 203. */
-{
-  int iset = 0;
-  float gset = 0.0;
-  float v1 = 0.0, v2 = 0.0, r = 0.0;
-  float factor = 0.0;
-
-  if (iset) {
-        iset = 0;
-        return gset;
-      	}
-  else {    
-        do {
-            v1 = (float)random(0.,1.) * 2.0 - 1.0;
-            v2 = (float)random(0.,1.) * 2.0 - 1.0;
-            r = v1 * v1 + v2 * v2;
-	                }
-        while (r > 1.0);
-        factor = sqrt (-2.0 * log (r) / r);
-        gset = v1 * factor;
-        iset = 1;
-        return (v2 * factor);
-    	}
-}
-\end
-
-\GenomeClass::display:
-	/*for(int i=0; i<SIZE; i++){
-		cout << Genome.x[i] << ":" << Genome.sigma[i] << "|";
-	}*/
-\end
-
-\Before everything else function:
-\end
-
-\After everything else function:
-\end
-
-\At the beginning of each generation function:
-  //cout << "At the beginning of each generation function called" << endl;
-\end
-  
-\At the end of each generation function:
-//cout << "At the end of each generation function called" << endl;
-\end
-
-\At each generation before reduce function:
-//cout << "At each generation before replacement function called" << endl;
-\end
- 
-\User classes :
-GenomeClass { 
-  float x[SIZE];
-  float sigma[SIZE]; // auto-adaptative mutation parameter
-}
-\end
-
-\GenomeClass::initialiser : // "initializer" is also accepted
-  for(int i=0; i<SIZE; i++ ) {
-     	Genome.x[i] = (float)random(X_MIN,X_MAX);
-	Genome.sigma[i]=(float)random(0.,0.5);
-	}
-\end
-
-\GenomeClass::crossover :
-  for (int i=0; i<SIZE; i++)
-  {
-    float alpha = (float)random(0.,1.); // barycentric crossover
-     child.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
-  }
-
-\end
-
-\GenomeClass::mutator : // Must return the number of mutations
-  int NbMut=0;
-  float pond = 1./sqrt((float)SIZE);
-
-    for (int i=0; i<SIZE; i++)
-    if (tossCoin(pMutPerGene)){
-    	NbMut++;
-       	Genome.sigma[i] = Genome.sigma[i] * exp(SIGMA*pond*(float)gauss());
-       	Genome.sigma[i] = MIN(0.5,Genome.sigma[0]);              
-       	Genome.sigma[i] = MAX(0.,Genome.sigma[0]);
-       	Genome.x[i] += Genome.sigma[i]*(float)gauss();
-       	Genome.x[i] = MIN(X_MAX,Genome.x[i]);              // pour eviter les depassements
-       	Genome.x[i] = MAX(X_MIN,Genome.x[i]);
-    	}
-return NbMut;
-\end
-
-\GenomeClass::evaluator : // Returns the score
-  float Score= 0.0;
-  Score= Weierstrass(Genome.x, SIZE);         
-  //Score= rosenbrock(Genome.x);         
-  return Score;
-\end
-
-\User Makefile options: 
-CPPFLAGS+=
-\end
-
-\Default run parameters :        // Please let the parameters appear in this order
-  Number of generations : 100           // NB_GEN
-  Time limit: 0                         // In seconds, 0 to deactivate
-  Population size : 3840                 //POP_SIZE
-  Offspring size : 3840 // 40% 
-  Mutation probability : 1       // MUT_PROB
-  Crossover probability : 1      // XOVER_PROB
-  Evaluator goal : minimise      // Maximise
-  Selection operator: Tournament 2.0
-  Surviving parents: 3840//percentage or absolute  
-  Surviving offspring: 3840
-  Reduce parents operator: Tournament 2.0
-  Reduce offspring operator: Tournament 2
-  Final reduce operator: Tournament 2
-
-  Elitism: Strong                       //Weak or Strong
-  Elite: 5
-
-  Print stats:true                         //Default: 1
-  Plot stats:false                          //Default: 0
-  Generate csv stats file:false
-  Generate gnuplot script:false
-  Generate R script:false
-
-\end
-