SecondRealEA.cpp

//-----------------------------------------------------------------------------
// SecondRealEA.cpp
//-----------------------------------------------------------------------------
//*
// Same code than FirstBitEA as far as Evolutionary Computation is concerned
// but now you learn to enter the parameters in a more flexible way
// (also slightly different than in SecondBitEA.cpp)
// and to twidle the output to your preferences (as in SecondBitEA.cpp)
//
//-----------------------------------------------------------------------------
#ifdef HAVE_CONFIG_H
#include <config.h>
#endif

// standard includes
#include <fstream>
#include <iostream>   // cout
#include <stdexcept>  // runtime_error

// the general include for eo
#include <eo>
#include <es.h>

// REPRESENTATION
//-----------------------------------------------------------------------------
// define your individuals
typedef eoReal<eoMinimizingFitness> Indi;

// Use functions from namespace std
using namespace std;

// EVALFUNC
//-----------------------------------------------------------------------------
// a simple fitness function that computes the euclidian norm of a real vector
// Now in a separate file, and declared as binary_value(const vector<bool> &)

#include "real_value.h"

// GENERAL
//-----------------------------------------------------------------------------

void main_function(int argc, char **argv)
{
// PARAMETRES
//-----------------------------------------------------------------------------
// instead of having all values of useful parameters as constants, read them:
// either on the command line (--option=value or -o=value)
//     or in a parameter file (same syntax, order independent,
//                             # = usual comment character
//     or in the environment (TODO)

  // First define a parser from the command-line arguments
  eoParser parser(argc, argv);

  // For each parameter, you can in on single line
  // define the parameter, read it through the parser, and assign it

  unsigned seed = parser.createParam(unsigned(time(0)), "seed", "Random number seed", 'S').value(); // will be in default section General

  // description of genotype
  unsigned vecSize = parser.createParam(unsigned(8), "vecSize", "Genotype size",'V', "Representation" ).value();

   // parameters for evolution engine
    unsigned popSize = parser.createParam(unsigned(10), "popSize", "Population size",'P', "Evolution engine" ).value();

    unsigned tSize = parser.createParam(unsigned(2), "tSize", "Tournament size",'T', "Evolution Engine" ).value();

   // init and stop
    string loadName = parser.createParam(string(""), "Load","A save file to restart from",'L', "Persistence" ).value();

    unsigned maxGen = parser.createParam(unsigned(100), "maxGen", "Maximum number of generations",'G', "Stopping criterion" ).value();

    unsigned minGen = parser.createParam(unsigned(100), "minGen", "Minimum number of generations",'g', "Stopping criterion" ).value();

    unsigned steadyGen = parser.createParam(unsigned(100), "steadyGen", "Number of generations with no improvement",'s', "Stopping criterion" ).value();

   // operators probabilities at the algorithm level
    double pCross = parser.createParam(double(0.6), "pCross", "Probability of Crossover", 'C', "Genetic Operators" ).value();

    double pMut = parser.createParam(double(0.1), "pMut", "Probability of Mutation", 'M', "Genetic Operators" ).value();

   // relative rates for crossovers
    double hypercubeRate = parser.createParam(double(1), "hypercubeRate", "Relative rate for hypercube crossover", '\0', "Genetic Operators" ).value();

    double segmentRate = parser.createParam(double(1), "segmentRate", "Relative rate for segment crossover", '\0', "Genetic Operators" ).value();

    // internal parameters for the mutations
    double EPSILON = parser.createParam(double(0.01), "EPSILON", "Width for uniform mutation", '\0', "Genetic Operators" ).value();

    double SIGMA = parser.createParam(double(0.3), "SIGMA", "Sigma for normal mutation", '\0', "Genetic Operators" ).value();

   // relative rates for mutations
    double uniformMutRate = parser.createParam(double(1), "uniformMutRate", "Relative rate for uniform mutation", '\0', "Genetic Operators" ).value();

    double detMutRate = parser.createParam(double(1), "detMutRate", "Relative rate for det-uniform mutation", '\0', "Genetic Operators" ).value();

    double normalMutRate = parser.createParam(double(1), "normalMutRate", "Relative rate for normal mutation", '\0', "Genetic Operators" ).value();

    // the name of the "status" file where all actual parameter values will be saved
    string str_status = parser.ProgramName() + ".status"; // default value
    string statusName = parser.createParam(str_status, "status","Status file",'S', "Persistence" ).value();

   // do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
   // i.e. in case you need parameters somewhere else, postpone these
    if (parser.userNeedsHelp())
      {
        parser.printHelp(cout);
        exit(1);
      }
    if (statusName != "")
      {
        ofstream os(statusName.c_str());
        os << parser;           // and you can use that file as parameter file
      }

// EVAL
  // Fitness function
  // Evaluation: from a plain C++ fn to an EvalFunc Object
  // you need to give the full description of the function
  eoEvalFuncPtr<Indi, double, const vector<double>& > plainEval(  real_value );
  // ... to an object that counts the nb of actual evaluations
  eoEvalFuncCounter<Indi> eval(plainEval);

// INIT
  // Initilisation of population
  // Either load or initialize
  // create an empty pop
  eoPop<Indi> pop;
  // create a state for reading
  eoState inState;              // a state for loading - WITHOUT the parser
  // register the rng and the pop in the state, so they can be loaded,
  // and the present run will be the exact conitnuation of the saved run
  // eventually with different parameters
  inState.registerObject(rng);
  inState.registerObject(pop);

  if (loadName != "")
    {
      inState.load(loadName); //  load the pop and the rng
      // the fitness is read in the file:
      // do only evaluate the pop if the fitness has changed
    }
  else
    {
      rng.reseed(seed);
      // a Indi random initializer
      // based on boolean_generator class (see utils/rnd_generator.h)
      eoUniformGenerator<double> uGen(-1.0, 1.0);
      eoInitFixedLength<Indi> random(vecSize, uGen);

      // Init pop from the randomizer: need to use the append function
      pop.append(popSize, random);
      // and evaluate pop (STL syntax)
      apply<Indi>(eval, pop);
    } // end of initializatio of the population

// OUTPUT
  // sort pop before printing it!
  pop.sort();
  // Print (sorted) intial population (raw printout)
  cout << "Initial Population" << endl;
  cout << pop;

// ENGINE
  // selection and replacement
// SELECT
  // The robust tournament selection
  eoDetTournamentSelect<Indi> selectOne(tSize);
  // is now encapsulated in a eoSelectPerc (entage)
  eoSelectPerc<Indi> select(selectOne);// by default rate==1

// REPLACE
  // And we now have the full slection/replacement - though with
  // no replacement (== generational replacement) at the moment :-)
  eoGenerationalReplacement<Indi> replace;

// OPERATORS
  // The variation operators
// CROSSOVER
  // uniform chooce on segment made by the parents
  eoSegmentCrossover<Indi> xoverS;
  // uniform choice in hypercube built by the parents
  eoHypercubeCrossover<Indi> xoverA;
  // Combine them with relative weights
  eoPropCombinedQuadOp<Indi> xover(xoverS, segmentRate);
  xover.add(xoverA, hypercubeRate, true);

// MUTATION
  // offspring(i) uniformly chosen in [parent(i)-epsilon, parent(i)+epsilon]
  eoUniformMutation<Indi>  mutationU(EPSILON);
  // k (=1) coordinates of parents are uniformly modified
  eoDetUniformMutation<Indi>  mutationD(EPSILON);
  // all coordinates of parents are normally modified (stDev SIGMA)
  eoNormalMutation<Indi>  mutationN(SIGMA);
  // Combine them with relative weights
  eoPropCombinedMonOp<Indi> mutation(mutationU, uniformMutRate);
  mutation.add(mutationD, detMutRate);
  mutation.add(mutationN, normalMutRate, true);

  // The operators are  encapsulated into an eoTRansform object
  eoSGATransform<Indi> transform(xover, pCross, mutation, pMut);

// STOP
  // termination condition see FirstBitEA.cpp
  eoGenContinue<Indi> genCont(maxGen);
  eoSteadyFitContinue<Indi> steadyCont(minGen, steadyGen);
  eoFitContinue<Indi> fitCont(0);
  eoCombinedContinue<Indi> continuator(genCont);
  continuator.add(steadyCont);
  continuator.add(fitCont);


// CHECKPOINT
  // but now you want to make many different things every generation
  // (e.g. statistics, plots, ...).
  // the class eoCheckPoint is dedicated to just that:

  // Declare a checkpoint (from a continuator: an eoCheckPoint
  // IS AN eoContinue and will be called in the loop of all algorithms)
  eoCheckPoint<Indi> checkpoint(continuator);

    // Create a counter parameter
    eoValueParam<unsigned> generationCounter(0, "Gen.");

    // Create an incrementor (sub-class of eoUpdater). Note that the
    // parameter's value is passed by reference,
    // so every time the incrementer is updated (every generation),
    // the data in generationCounter will change.
    eoIncrementor<unsigned> increment(generationCounter.value());

    // Add it to the checkpoint,
    // so the counter is updated (here, incremented) every generation
    checkpoint.add(increment);

    // now some statistics on the population:
    // Best fitness in population
    eoBestFitnessStat<Indi> bestStat;
    // Second moment stats: average and stdev
    eoSecondMomentStats<Indi> SecondStat;

    // Add them to the checkpoint to get them called at the appropriate time
    checkpoint.add(bestStat);
    checkpoint.add(SecondStat);

    // The Stdout monitor will print parameters to the screen ...
    eoStdoutMonitor monitor(false);

    // when called by the checkpoint (i.e. at every generation)
    checkpoint.add(monitor);

    // the monitor will output a series of parameters: add them
    monitor.add(generationCounter);
    monitor.add(eval);          // because now eval is an eoEvalFuncCounter!
    monitor.add(bestStat);
    monitor.add(SecondStat);

    // A file monitor: will print parameters to ... a File, yes, you got it!
    eoFileMonitor fileMonitor("stats.xg", " ");

    // the checkpoint mechanism can handle multiple monitors
    checkpoint.add(fileMonitor);

    // the fileMonitor can monitor parameters, too, but you must tell it!
    fileMonitor.add(generationCounter);
    fileMonitor.add(bestStat);
    fileMonitor.add(SecondStat);

    // Last type of item the eoCheckpoint can handle: state savers:
    eoState outState;
    // Register the algorithm into the state (so it has something to save!!)
    outState.registerObject(parser);
    outState.registerObject(pop);
    outState.registerObject(rng);

    // and feed the state to state savers
    // save state every 100th  generation
    eoCountedStateSaver stateSaver1(20, outState, "generation");
    // save state every 1 seconds
    eoTimedStateSaver   stateSaver2(1, outState, "time");

    // Don't forget to add the two savers to the checkpoint
    checkpoint.add(stateSaver1);
    checkpoint.add(stateSaver2);
    // and that's it for the (control and) output

// GENERATION
  // the algorithm

  // Easy EA requires
  // stopping criterion, eval, selection, transformation, replacement
  eoEasyEA<Indi> gga(checkpoint, eval, select, transform, replace);

  // Apply algo to pop - that's it!
  gga(pop);

// OUTPUT
  // Print (sorted) intial population
  pop.sort();
  cout << "FINAL Population\n" << pop << endl;
// GENERAL
}

// A main that catches the exceptions

int main(int argc, char **argv)
{
    try
    {
        main_function(argc, argv);
    }
    catch(exception& e)
    {
        cout << "Exception: " << e.what() << '\n';
    }

    return 1;
}

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