Changeset 1008 for cpp/frams

Timestamp:

07/14/20 20:20:54 (4 years ago)

Author:

Maciej Komosinski

Message:

Implemented crossing over, handled failed mutations and crossovers, added printing extensive statistics

File:

• cpp/frams/_demos/evol_test.cpp (modified) (9 diffs)

cpp/frams/_demos/evol_test.cpp

@@ -5 +5 @@
 
 #include <vector>
+#include <numeric> //std::accumulate()
 #include "common/loggers/loggertostdout.h"
 #include "frams/genetics/preconfigured.h"
@@ -22 +23 @@
 }
 
-void evaluate_fitness(Individual &ind, const char *fitness_def)
+double get_fitness(const Individual &ind, const char *fitness_def)
 {
         SString genotype = ind.geno.getGenes();
@@ -32 +33 @@
                 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':
@@ -59 +65 @@
                 p++;
         }
-        ind.fitness = fitness;
+        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()));
 }
 
@@ -76 +99 @@
 
 
-/** A minimalistic steady-state evolutionary algorithm. */
+// A minimalistic steady-state evolutionary algorithm.
 int main(int argc, char *argv[])
 {
@@ -93 +116 @@
                 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 0.5 0.5 4 NC\n\n\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");
@@ -103 +127 @@
                 //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 contains 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;
         }
@@ -126 +154 @@
                         return 2;
                 }
-                evaluate_fitness(ind, fitness_def);
+                update_fitness(ind, fitness_def);
         }
         for (int i = 0; i < nr_evals; i++)
@@ -136 +164 @@
                 if (rnd < prob_mut)
                 {
-                        population[selected_negative].geno = genman.mutate(population[selected_positive].geno);
-                        //TODO handle failed mutation
-                        evaluate_fitness(population[selected_negative], fitness_def);
+                        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
                 {
-                        //TODO crossover
+                        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\t...\n", i); //TODO print min,avg,max fitness \t min,avg,max genotype length \t min,avg,max parts \t min,avg,max neurons
+                {
+                        printf("Evaluation %d\t", i);
+                        for (char c : string("!PJNCL"))
+                        {
+                                print_stats(population, c);
+                                printf("\t");
+                        }
+                        printf("\n");
+                }
         }
         for (const Individual& ind : population)