Basic onemax

/*_________________________________________________________

onemax.ez // Evolve individuals containing 111111111111111111...
__________________________________________________________*/

\User declarations :
#define SIZE 1000
 
float pMutPerGene=1.0/SIZE;     // Good probability of mutation per gene on an unknown problem
\end

\User functions:
\end

\User CUDA:
\end

\Before everything else function:
                                // read some input file here
\end

\After everything else function:
                                // save the results in a file here
\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 {                   // Reserved class name
  int x[SIZE];
}
\end

\GenomeClass::display:
\end

\GenomeClass::initialiser :     // "initializer" is also accepted
  for(int i=0; i<SIZE; i++ )
    Genome.x[i] = random(0,2);  // random function returns a value in [x,y[
\end

\GenomeClass::crossover :       // Reserved variable child is a clone of parent1
  int nLocus=random(1,SIZE);    // It is only needed to insert values from parent 2
  for (int i=nLocus;i<SIZE;i++) // into the child
    child.x[i]=parent2.x[i];
\end

\GenomeClass::mutator :         // Must return a value (for historical reasons)
  for (int i=0;i<SIZE;i++)      
    if (tossCoin(pMutPerGene))  // tosscoin(p) will return 1 with p probability 
      Genome.x[i]=(Genome.x[i]+1)%2;
  return 3;                     // Any value will do
\end

\GenomeClass::evaluator :       // Returns the score
  float fScore=0;         
  for (int i=0;i<SIZE;i++)
    fScore+=Genome.x[i];        // for a onemax, the score is the sum of the genes
  return fScore;
\end

\User Makefile options: 
\end

\Default run parameters :       // Many of these parameters are command line parameters  
  Number of generations : 100   // NB_GEN global variable in the code
  Time limit: 0 			          // In seconds, 0 to deactivate
  Population size : 100			    // POP_SIZE global variable in the code
  Offspring size : 100          // percentage (with %) or absolute (without)
  Mutation probability : 1      // MUT_PROB global variable in the code
  Crossover probability : 1     // XOVER_PROB global variable in the code
  Evaluator goal : maximise     // or minimise

  Surviving parents: 100%       // selection of breeders
  Reduce parents operator: Tournament 2 

  Selection operator: Tournament 2 // parents selection to create offspring population

  Surviving offspring: 100%     // selection of offspring for populating next generation
  Reduce offspring operator: Tournament 2 

  Final reduce operator: Tournament 2 // selection from parents and offspring to create next generation

  Elitism: Strong               // or Weak
  Elite: 1
  Print stats: true             // or false
  Generate csv stats file: false			
  Generate gnuplot script: false
  Generate R script:false
  Plot stats:true               // Default false

  Remote island model: false
  IP file: ip.txt               // File containing all the remote island's IP
  Server port : 2929
  Migration probability: 0.3

  Save population: false
  Start from file:false
\end