325 lines
12 KiB
C++
325 lines
12 KiB
C++
//-----------------------------------------------------------------------------
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// SecondRealEA.cpp
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//-----------------------------------------------------------------------------
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//*
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// Same code than FirstBitEA as far as Evolutionary Computation is concerned
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// but now you learn to enter the parameters in a more flexible way
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// (also slightly different than in SecondBitEA.cpp)
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// and to twidle the output to your preferences (as in SecondBitEA.cpp)
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//
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//-----------------------------------------------------------------------------
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// standard includes
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#include <stdexcept> // runtime_error
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#include <iostream> // cout
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#include <strstream> // ostrstream, istrstream
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// the general include for eo
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#include <eo>
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#include <es.h>
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// REPRESENTATION
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//-----------------------------------------------------------------------------
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// define your individuals
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typedef eoReal<eoMinimizingFitness> Indi;
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// Use functions from namespace std
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using namespace std;
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// EVALFUNC
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//-----------------------------------------------------------------------------
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// a simple fitness function that computes the euclidian norm of a real vector
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// Now in a separate file, and declared as binary_value(const vector<bool> &)
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#include "real_value.h"
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// GENERAL
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//-----------------------------------------------------------------------------
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void main_function(int argc, char **argv)
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{
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// PARAMETRES
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//-----------------------------------------------------------------------------
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// instead of having all values of useful parameters as constants, read them:
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// either on the command line (--option=value or -o=value)
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// or in a parameter file (same syntax, order independent,
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// # = usual comment character
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// or in the environment (TODO)
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// First define a parser from the command-line arguments
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eoParser parser(argc, argv);
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// For each parameter, you can in on single line
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// define the parameter, read it through the parser, and assign it
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unsigned seed = parser.createParam(unsigned(time(0)), "seed", "Random number seed", 'S').value(); // will be in default section General
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// description of genotype
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unsigned vecSize = parser.createParam(unsigned(8), "vecSize", "Genotype size",'V', "Representation" ).value();
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// parameters for evolution engine
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unsigned popSize = parser.createParam(unsigned(10), "popSize", "Population size",'P', "Evolution engine" ).value();
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unsigned tSize = parser.createParam(unsigned(2), "tSize", "Tournament size",'T', "Evolution Engine" ).value();
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// init and stop
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string loadName = parser.createParam(string(""), "Load","A save file to restart from",'L', "Persistence" ).value();
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unsigned maxGen = parser.createParam(unsigned(100), "maxGen", "Maximum number of generations",'G', "Stopping criterion" ).value();
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unsigned minGen = parser.createParam(unsigned(100), "minGen", "Minimum number of generations",'g', "Stopping criterion" ).value();
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unsigned steadyGen = parser.createParam(unsigned(100), "steadyGen", "Number of generations with no improvement",'s', "Stopping criterion" ).value();
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// operators probabilities at the algorithm level
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double pCross = parser.createParam(double(0.6), "pCross", "Probability of Crossover", 'C', "Genetic Operators" ).value();
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double pMut = parser.createParam(double(0.1), "pMut", "Probability of Mutation", 'M', "Genetic Operators" ).value();
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// relative rates for crossovers
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double hypercubeRate = parser.createParam(double(1), "hypercubeRate", "Relative rate for hypercube crossover", '\0', "Genetic Operators" ).value();
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double segmentRate = parser.createParam(double(1), "segmentRate", "Relative rate for segment crossover", '\0', "Genetic Operators" ).value();
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// internal parameters for the mutations
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double EPSILON = parser.createParam(double(0.01), "EPSILON", "Width for uniform mutation", '\0', "Genetic Operators" ).value();
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double SIGMA = parser.createParam(double(0.3), "SIGMA", "Sigma for normal mutation", '\0', "Genetic Operators" ).value();
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// relative rates for mutations
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double uniformMutRate = parser.createParam(double(1), "uniformMutRate", "Relative rate for uniform mutation", '\0', "Genetic Operators" ).value();
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double detMutRate = parser.createParam(double(1), "detMutRate", "Relative rate for det-uniform mutation", '\0', "Genetic Operators" ).value();
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double normalMutRate = parser.createParam(double(1), "normalMutRate", "Relative rate for normal mutation", '\0', "Genetic Operators" ).value();
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// the name of the "status" file where all actual parameter values will be saved
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string str_status = parser.ProgramName() + ".status"; // default value
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string statusName = parser.createParam(str_status, "status","Status file",'S', "Persistence" ).value();
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// do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
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// i.e. in case you need parameters somewhere else, postpone these
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if (parser.userNeedsHelp())
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{
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parser.printHelp(cout);
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exit(1);
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}
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if (statusName != "")
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{
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ofstream os(statusName.c_str());
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os << parser; // and you can use that file as parameter file
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}
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// EVAL
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/////////////////////////////
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// Fitness function
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////////////////////////////
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// Evaluation: from a plain C++ fn to an EvalFunc Object
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// you need to give the full description of the function
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eoEvalFuncPtr<Indi, double, const vector<double>& > plainEval( real_value );
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// ... to an object that counts the nb of actual evaluations
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eoEvalFuncCounter<Indi> eval(plainEval);
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// INIT
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////////////////////////////////
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// Initilisation of population
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////////////////////////////////
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// Either load or initialize
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// create an empty pop
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eoPop<Indi> pop;
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// create a state for reading
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eoState inState; // a state for loading - WITHOUT the parser
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// register the rng and the pop in the state, so they can be loaded,
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// and the present run will be the exact conitnuation of the saved run
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// eventually with different parameters
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inState.registerObject(rng);
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inState.registerObject(pop);
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if (loadName != "")
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{
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inState.load(loadName); // load the pop and the rng
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// the fitness is read in the file:
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// do only evaluate the pop if the fitness has changed
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}
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else
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{
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rng.reseed(seed);
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// a Indi random initializer
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// based on boolean_generator class (see utils/rnd_generator.h)
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eoUniformGenerator<double> uGen(-1.0, 1.0);
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eoInitFixedLength<Indi> random(vecSize, uGen);
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// Init pop from the randomizer: need to use the append function
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pop.append(popSize, random);
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// and evaluate pop (STL syntax)
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apply<Indi>(eval, pop);
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} // end of initializatio of the population
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// OUTPUT
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// sort pop before printing it!
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pop.sort();
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// Print (sorted) intial population (raw printout)
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cout << "Initial Population" << endl;
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cout << pop;
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// ENGINE
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/////////////////////////////////////
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// selection and replacement
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////////////////////////////////////
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// SELECT
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// The robust tournament selection
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eoDetTournamentSelect<Indi> selectOne(tSize);
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// is now encapsulated in a eoSelectPerc (entage)
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eoSelectPerc<Indi> select(selectOne);// by default rate==1
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// REPLACE
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// And we now have the full slection/replacement - though with
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// no replacement (== generational replacement) at the moment :-)
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eoGenerationalReplacement<Indi> replace;
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// OPERATORS
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//////////////////////////////////////
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// The variation operators
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//////////////////////////////////////
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// CROSSOVER
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// uniform chooce on segment made by the parents
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eoSegmentCrossover<Indi> xoverS;
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// uniform choice in hypercube built by the parents
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eoHypercubeCrossover<Indi> xoverA;
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// Combine them with relative weights
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eoPropCombinedQuadOp<Indi> xover(xoverS, segmentRate);
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xover.add(xoverA, hypercubeRate, true);
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// MUTATION
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// offspring(i) uniformly chosen in [parent(i)-epsilon, parent(i)+epsilon]
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eoUniformMutation<Indi> mutationU(EPSILON);
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// k (=1) coordinates of parents are uniformly modified
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eoDetUniformMutation<Indi> mutationD(EPSILON);
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// all coordinates of parents are normally modified (stDev SIGMA)
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eoNormalMutation<Indi> mutationN(SIGMA);
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// Combine them with relative weights
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eoPropCombinedMonOp<Indi> mutation(mutationU, uniformMutRate);
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mutation.add(mutationD, detMutRate);
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mutation.add(mutationN, normalMutRate, true);
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// The operators are encapsulated into an eoTRansform object
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eoSGATransform<Indi> transform(xover, pCross, mutation, pMut);
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// STOP
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//////////////////////////////////////
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// termination condition see FirstBitEA.cpp
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/////////////////////////////////////
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eoGenContinue<Indi> genCont(maxGen);
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eoSteadyFitContinue<Indi> steadyCont(minGen, steadyGen);
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eoFitContinue<Indi> fitCont(0);
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eoCombinedContinue<Indi> continuator(genCont);
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continuator.add(steadyCont);
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continuator.add(fitCont);
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// CHECKPOINT
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// but now you want to make many different things every generation
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// (e.g. statistics, plots, ...).
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// the class eoCheckPoint is dedicated to just that:
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// Declare a checkpoint (from a continuator: an eoCheckPoint
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// IS AN eoContinue and will be called in the loop of all algorithms)
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eoCheckPoint<Indi> checkpoint(continuator);
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// Create a counter parameter
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eoValueParam<unsigned> generationCounter(0, "Gen.");
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// Create an incrementor (sub-class of eoUpdater). Note that the
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// parameter's value is passed by reference,
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// so every time the incrementer is updated (every generation),
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// the data in generationCounter will change.
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eoIncrementor<unsigned> increment(generationCounter.value());
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// Add it to the checkpoint,
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// so the counter is updated (here, incremented) every generation
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checkpoint.add(increment);
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// now some statistics on the population:
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// Best fitness in population
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eoBestFitnessStat<Indi> bestStat;
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// Second moment stats: average and stdev
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eoSecondMomentStats<Indi> SecondStat;
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// Add them to the checkpoint to get them called at the appropriate time
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checkpoint.add(bestStat);
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checkpoint.add(SecondStat);
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// The Stdout monitor will print parameters to the screen ...
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eoStdoutMonitor monitor(false);
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// when called by the checkpoint (i.e. at every generation)
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checkpoint.add(monitor);
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// the monitor will output a series of parameters: add them
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monitor.add(generationCounter);
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monitor.add(eval); // because now eval is an eoEvalFuncCounter!
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monitor.add(bestStat);
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monitor.add(SecondStat);
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// A file monitor: will print parameters to ... a File, yes, you got it!
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eoFileMonitor fileMonitor("stats.xg", " ");
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// the checkpoint mechanism can handle multiple monitors
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checkpoint.add(fileMonitor);
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// the fileMonitor can monitor parameters, too, but you must tell it!
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fileMonitor.add(generationCounter);
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fileMonitor.add(bestStat);
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fileMonitor.add(SecondStat);
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// Last type of item the eoCheckpoint can handle: state savers:
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eoState outState;
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// Register the algorithm into the state (so it has something to save!!)
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outState.registerObject(parser);
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outState.registerObject(pop);
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outState.registerObject(rng);
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// and feed the state to state savers
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// save state every 100th generation
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eoCountedStateSaver stateSaver1(20, outState, "generation");
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// save state every 1 seconds
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eoTimedStateSaver stateSaver2(1, outState, "time");
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// Don't forget to add the two savers to the checkpoint
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checkpoint.add(stateSaver1);
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checkpoint.add(stateSaver2);
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// and that's it for the (control and) output
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// GENERATION
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/////////////////////////////////////////
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// the algorithm
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////////////////////////////////////////
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// Easy EA requires
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// stopping criterion, eval, selection, transformation, replacement
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eoEasyEA<Indi> gga(checkpoint, eval, select, transform, replace);
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// Apply algo to pop - that's it!
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gga(pop);
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// OUTPUT
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// Print (sorted) intial population
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pop.sort();
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cout << "FINAL Population\n" << pop << endl;
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// GENERAL
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}
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// A main that catches the exceptions
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int main(int argc, char **argv)
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{
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try
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{
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main_function(argc, argv);
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}
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catch(exception& e)
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{
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cout << "Exception: " << e.what() << '\n';
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}
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return 1;
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}
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