Environmental modeling and software paper supplementary material

/*_________________________________________________________

 This is a standard GP implementation on EASEA, 
 aimed for regression.

 For running on CPU, compile with:
 $ make easeaclean ; easena -gp regression.ez ; make

 Then run with:
 $ ./regression --seed 2      // for a nice result :-)

 You can test the parallel version on several cores with:
 $ ./regression --seed 2 --nbCPUThreads 20

 For running on GPU, compile with:	
 $ make easeaclean ; easena -cuda_gp regression.ez ; make

__________________________________________________________*/

\User declarations :

1. include<stdio.h>
2. include<string.h>

// these 3 defines are mandatory here. Adjust as you like.

1. define NO_FITNESS_CASES 18
2. define VAR_LEN 1 // number of input variables
3. define GROW_FULL_RATIO 0.5 // cf. Koza's book on Genetic Programming

1. define NUMTHREAD 1024 // nb of threads in parallel on GPU cards.

  // 1024 is the maximum on Fermi architectures. It is 512 on older cards

1. define MAX_STACK 15

1. define PI (3.141592653589793)

float x[NO_FITNESS_CASES] = {0.0833, 0.1667, 0.25, 0.3333, 0.4167, 0.5, 0.5833, 0.6667, 0.75, 0.8333, 0.9167, 1, 1.0833, 1.1667, 1.25, 1.3333, 1.4167, 1.5};

float y[NO_FITNESS_CASES] = { -0.457142857, -0.428571429, -0.285714286, -0.042857143, 0.142857143, 0.328571429, 0.457142857, 0.571428571, 0.742857143, 0.914285714, 1.128571429, 1.271428571, 1.171428571, 0.985714286, 0.714285714, 0.442857143, 0.2, 0.057142857};

\end

\User functions:

int initData(float*** inputs, float** outputs) {

 int i=0;
 (*inputs) = new float*[NO_FITNESS_CASES];
 (*outputs) = new float[NO_FITNESS_CASES];
   
 for( i=0 ; i<NO_FITNESS_CASES ; i++ ){
   (*inputs)[i]=new float[VAR_LEN];
   (*inputs)[i][0] = x[i];
   (*outputs)[i] = y[i];
 }

 return NO_FITNESS_CASES;

}

void free_data(){

 for( int i=0 ; i<NO_FITNESS_CASES ;i++ ) delete[] inputs[i] ;
 delete[] outputs;
 delete[] inputs;

}

\end

\Before everything else function:

 initData(&inputs,&outputs); // inputs, outputs requis par EASEA

\end

\After everything else function:

 std::cout << "y=" << toString(((IndividualImpl*)EA->population->Best)->root) << std::endl;
 free_data();

\end

\At the beginning of each generation function: \end

\At the end of each generation function: \end

\At each generation before reduce function: \end

\User classes :

GenomeClass {

 GPNode* root; // GPNode is a reserved name that must be used for GP

} \end

\GenomeClass::display: \end

\GenomeClass::initialiser :

 Genome.root = ramped_hh(); // Initializer, cf. Koza GP

\end

\GenomeClass::crossover :

 simpleCrossOver(parent1,parent2,child); // cf. Koza GP
 child.valid = false;       // to force the evaluation of the child

\end

\GenomeClass::mutator :

 simple_mutator(&Genome); // cf. Koza GP

\end

\begin operator description : // <symbole>, "<affichage dans l'arbre>", <arité de l'opérateur>, {RESULT=<resultat>} OP_X, "x", 0, {RESULT=INPUT[0];}; OP_ERC, "ERC", 0, {RESULT=ERC;}; // ERC = valeur aléatoire entre 0 et 1 OP_ADD, "+", 2, {RESULT=OP1+OP2;}; OP_SUB, "-", 2, {RESULT=OP1-OP2;}; OP_MUL, "*", 2, {RESULT=OP1*OP2;}; OP_SIN, "sin", 1, {RESULT=sin(OP1);};

// you can add other operators if you wish \end

\GenomeClass::evaluator header: \end

\GenomeClass::evaluator for each fc : // How to compute error for one point float expected_value = OUTPUT; error = pow((expected_value-EVOLVED_VALUE),2); error/=(float)NO_FITNESS_CASES; error=sqrtf(error); \end

\GenomeClass::evaluator accumulator : // here, error is the sum of errors for each point

return error; \end

\User Makefile options: CXXFLAGS+=-I/usr/local/cuda/common/inc/ -I/usr/local/cuda/include/ LDFLAGS+= \end

\Default run parameters :

 Number of generations : 500   	          // NB_GEN
 Time limit: 0 			                    // In seconds, 0 to deactivate
 Population size : 50000			            // POP_SIZE
 Offspring size : 50000                   // can be a percentage such as 40% 
 Mutation probability : 0.1              // Probability to call the mutation function
 Crossover probability : 1               // Probability to call the crossover function
 Evaluator goal : minimise               // or Maximise
 Selection operator: Tournament 10        // to select parents
 Surviving parents: 100%                 // to select breeders
 Surviving offspring: 100%               // to select among offspring for next generation
 Reduce parents operator: Tournament 2   // how to select the breeders
 Reduce offspring operator: Tournament 2 // how to select the offspring that will compete to access the next generation
 Final reduce operator: Tournament 2     // to select the individuals composing the next generation

 Elitism: Strong			                    // Strong = from parents pop, Weak = from parents + children
 Elite: 1
 Print stats: true
 Generate csv stats file:false			
 Generate gnuplot script:false
 Generate R script:false
 Plot stats:true	

 Remote island model: false
 IP file: ip.txt 			//File containing all the remote island's IP
 Server port : 2929
 Migration probability: 0.33

 Save population: false
 Start from file: false

 // nouveaux paramètres
 max init tree depth : 4 
 min init tree depth : 2
 max tree depth : 6

 size of prog buffer : 200000000

\end