source: cpp/frams/_demos/evol_test.cpp @ 1026

// This file is a part of Framsticks SDK.  http://www.framsticks.com/
// Copyright (C) 2019-2020  Maciej Komosinski and Szymon Ulatowski.
// See LICENSE.txt for details.


#include <vector>
#include <numeric> //std::accumulate()
#include "common/loggers/loggertostdout.h"
#include "frams/genetics/preconfigured.h"
#include "frams/genetics/genman.h"
#include "frams/model/model.h"


struct Individual
{
        Geno geno;
        double fitness;
};

double criterion(char symbol, double value)
{
        return isupper(symbol) ? value : -value;
}

double get_fitness(const Individual &ind, const char *fitness_def)
{
        SString genotype = ind.geno.getGenes();
        Model model = Model(ind.geno, Model::SHAPETYPE_UNKNOWN);
        double fitness = 0;
        const char *p = fitness_def;
        while (*p)
        {
                switch (*p)
                {
                case '0':
                        break;
                case '!': //special symbol for current fitness (used only in printing population stats)
                        fitness += ind.fitness;
                        break;
                case 'l':
                case 'L':
                        fitness += criterion(*p, genotype.length());
                        break;
                case 'p':
                case 'P':
                        fitness += criterion(*p, model.getPartCount());
                        break;
                case 'j':
                case 'J':
                        fitness += criterion(*p, model.getJointCount());
                        break;
                case 'n':
                case 'N':
                        fitness += criterion(*p, model.getNeuroCount());
                        break;
                case 'c':
                case 'C':
                        fitness += criterion(*p, model.getConnectionCount());
                        break;
                        // TODO add more criteria as described in main() below
                default:
                        printf("Unknown fitness criterion symbol: '%c'\n", *p);
                        exit(3);
                }
                p++;
        }
        return fitness;
}

void update_fitness(Individual &ind, const char *fitness_def)
{
        ind.fitness = get_fitness(ind, fitness_def);
}

void print_stats(const vector<Individual> &population, char criterion)
{
        vector<double> criterion_values;
        char crit[2] = { 0 };
        crit[0] = criterion;
        for (const Individual& ind : population)
                criterion_values.push_back(get_fitness(ind, crit));
        printf("%g,%g,%g", *std::min_element(criterion_values.begin(), criterion_values.end()),
                std::accumulate(criterion_values.begin(), criterion_values.end(), 0.0) / criterion_values.size(),
                *std::max_element(criterion_values.begin(), criterion_values.end()));
}

int tournament(const vector<Individual> &population, int tournament_size)
{
        int best = -1;
        for (int i = 0; i < tournament_size; i++)
        {
                int rnd = rndUint(population.size());
                if (best == -1) best = rnd;
                else if (population[rnd].fitness > population[best].fitness) //assume maximization
                        best = rnd;
        }
        return best;
}


// A minimalistic steady-state evolutionary algorithm.
int main(int argc, char *argv[])
{
        PreconfiguredGenetics genetics;
        LoggerToStdout messages_to_stdout(LoggerBase::Enable);
        GenMan genman;

        bool deterministic;
        int pop_size, nr_evals;
        double prob_mut, prob_xover;
        const char* format;
        const char* fitness_def;

        if (argc < 8)
        {
                printf("Too few parameters!\n");
                printf("Command line: <deterministic?_0_or_1> <population_size> <nr_evaluations> <prob_mut> <prob_xover> <genetic_format> <fitness_definition>\n");
                printf("Example: 1 10 50 0.6 0.4 4 NC\n\n");
                printf("Fitness definition is a sequence of capital (+1 weight) and small (-1 weight) letters.\n");
                printf("Each letter corresponds to one fitness criterion, and they are all weighted and added together.\n");
                printf("  0      - a constant value of 0 that provides a flat fitness landscape (e.g. for testing biases of genetic operators).\n");
                printf("  l or L - genotype length in characters.\n");
                printf("  p or P - the number of Parts.\n");
                printf("  j or J - the number of Joints.\n");
                printf("  n or N - the number of Neurons.\n");
                printf("  c or C - the number of neural Connections.\n");
                //TODO add b - bounding box volume (from Model), s - surface area (from geometry), v - volume (from geometry), h,w,d - three consecutive dimensions (from geometry)

                printf("\nThe output consists of 7 columns separated by the TAB character.\n");
                printf("The first column is the number of mutated or crossed over and evaluated genotypes.\n");
                printf("The remaining columns are triplets of min,avg,max (in the population) of fitness, Parts, Joints, Neurons, Connections, genotype characters.\n");
                printf("Finally, the genotypes in the last population are printed with their fitness values.\n");
                return 1;
        }

        deterministic = atoi(argv[1]) == 1;
        pop_size = atoi(argv[2]);
        nr_evals = atoi(argv[3]);
        prob_mut = atof(argv[4]);
        prob_xover = atof(argv[5]);
        format = argv[6];
        fitness_def = argv[7];

        if (!deterministic)
                rndGetInstance().randomize();

        vector<Individual> population(pop_size);
        for (Individual& ind : population)
        {
                ind.geno = genman.getSimplest(format);
                if (ind.geno.getGenes() == "")
                {
                        printf("Could not get the simplest genotype for format '%s'\n", format);
                        return 2;
                }
                update_fitness(ind, fitness_def);
        }
        for (int i = 0; i < nr_evals; i++)
        {
                int selected_positive = tournament(population, max(2, int(sqrt(population.size()) / 2))); //moderate positive selection pressure
                int selected_negative = rndUint(population.size()); //random negative selection

                double rnd = rndDouble(prob_mut + prob_xover);
                if (rnd < prob_mut)
                {
                        Geno mutant = genman.mutate(population[selected_positive].geno);
                        if (mutant.getGenes() == "")
                        {
                                printf("Failed mutation (%s) of '%s'\n", mutant.getComment().c_str(), population[selected_positive].geno.getGenes().c_str());
                        }
                        else
                        {
                                population[selected_negative].geno = mutant;
                                update_fitness(population[selected_negative], fitness_def);
                        }
                }
                else
                {
                        int selected_positive2 = tournament(population, max(2, int(sqrt(population.size()) / 2)));
                        Geno xover = genman.crossOver(population[selected_positive].geno, population[selected_positive2].geno);
                        if (xover.getGenes() == "")
                        {
                                printf("Failed crossover (%s) of '%s' and '%s'\n", xover.getComment().c_str(), population[selected_positive].geno.getGenes().c_str(), population[selected_positive2].geno.getGenes().c_str());
                        }
                        else
                        {
                                population[selected_negative].geno = xover;
                                update_fitness(population[selected_negative], fitness_def);
                        }
                }

                if (i % population.size() == 0 || i == nr_evals - 1)
                {
                        printf("Evaluation %d", i);
                        for (char c : string("!PJNCL"))
                        {
                                printf("\t");
                                print_stats(population, c);
                        }
                        printf("\n");
                }
        }
        for (const Individual& ind : population)
        {
                printf("%.1f\t", ind.fitness);
                printf("%s\n", ind.geno.getGenesAndFormat().c_str());
        }

        return 0;
}