-
Notifications
You must be signed in to change notification settings - Fork 1
/
geneticalgorithm.cpp
131 lines (107 loc) · 3.57 KB
/
geneticalgorithm.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
/*
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public
License version 2 as published by the Free Software Foundation.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Library General Public License
along with this library; see the file COPYING.LIB. If not, write to
the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor,
Boston, MA 02110-1301, USA.
*/
#include <vector>
#include <iostream>
#include <cmath>
#include <cstdlib>
#include <algorithm>
#include "random.h"
using namespace Tai;
using namespace std;
template <class Gene, class FitnessFunction>
GeneticAlgorithm<Gene,FitnessFunction>::GeneticAlgorithm(const FitnessFunction& ff) : fitnessFunction(ff)
{
}
template <class Gene, class FitnessFunction>
GeneticAlgorithm<Gene,FitnessFunction>::~GeneticAlgorithm()
{
}
template <class Gene, class FitnessFunction>
void GeneticAlgorithm<Gene,FitnessFunction>::iterate()
{
// Selection for crossover
// Mutation
// Fitness Calculation
// Selection of next generation
int populationSize = population.size();
double mutateProb = 0.75;
double mutateFactor = 0.05;
double growProb = 0.01;
double shrinkProb = 0.4;
if (m_vResults.size() > 0)
{
int index = std::min<int>(m_vResults.size(), 20);
double delta = ( m_vResults[m_vResults.size() - index] - *(m_vResults.rbegin()))/(1.0*index);
growProb = 0.05/(1.0 + pow(2, delta/100.0 ));
mutateFactor = 0.05/(1.0 + pow(2, -delta/20.0 + 5));
mutateProb = 0.5/(1.0 + pow(2, -delta/20.0 + 5));
}
cout << "mutateProb=" << mutateProb << ", ";
cout << "mutateFactor=" << mutateFactor << ", ";
cout << "growProb=" << growProb << ", ";
cout << "shrinkProb=" << shrinkProb << endl;
for (int cnt = 0; cnt < populationSize; ++cnt)
{
/*int mate = int(randomd()*populationSize);
while (mate == cnt && populationSize > 2)
{
mate = int(randomd()*populationSize);
}*/
/*
Gene offspring;
if (randomd() > 0.5)
{
offspring = Gene::crossOver(population[cnt], population[mate]);
}
else
{
offspring = population[cnt];
}
//*/
Gene offspring = population[cnt];
//Gene offspring(population[cnt]);// = Gene::crossOver(population[cnt], population[mate]);
// mutateProb, mutateFactor, growProb, shrinkProb
offspring.mutate(mutateProb, mutateFactor, growProb, shrinkProb);
//offspring.mutate(0.1, 0.5*(cnt + 1.0)/populationSize);
fitnessFunction.calculate(offspring);
if (offspring.fitness() < population[cnt].fitness())
{
population[cnt] = offspring;
}
//population.push_back(offspring);
}
/*typename vector<Gene>::iterator gene_it = population.begin();
for (; gene_it != population.end(); ++gene_it)
{
fitnessFunction.calculate(*gene_it);
}*/
sort(population.begin(), population.end(), MoreFit());
m_vResults.push_back(bestGene().fitness());
// kill of weaker part of population
/*while (population.size() > populationSize)
{
population.pop_back();
}*/
}
template<class Gene, class FitnessFunction>
void GeneticAlgorithm<Gene,FitnessFunction>::setInitialPopulation(const vector<Gene>& initialPopulation)
{
population = initialPopulation;
typename vector<Gene>::iterator gene_it = population.begin();
for (; gene_it != population.end(); ++gene_it)
{
fitnessFunction.calculate(*gene_it);
}
sort(population.begin(), population.end(), MoreFit());
}