-
Notifications
You must be signed in to change notification settings - Fork 1
/
GeneticAlgorithm.java
242 lines (211 loc) · 5.95 KB
/
GeneticAlgorithm.java
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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
package Optimisation;
import javax.swing.*;
import java.awt.*;
import java.util.*;
import java.util.List;
public class GeneticAlgorithm extends JFrame {
Random rnd = new Random(1);
int n = rnd.nextInt(300) + 250;
int generation;
double[] x = new double[n];
double[] y = new double[n];
int[] bestState;
{
for (int i = 0; i < n; i++) {
x[i] = rnd.nextDouble();
y[i] = rnd.nextDouble();
}
}
public void geneticAlgorithm() {
bestState = new int[n];
for (int i = 0; i < n; i++)
bestState[i] = i;
final int populationLimit = 100;
final Population population = new Population(populationLimit);
final int n = x.length;
for (int i = 0; i < populationLimit; i++)
population.chromosomes.add(new Chromosome(optimize(getRandomPermutation(n))));
final double mutationRate = 0.3;
final int generations = 10_000;
for (generation = 0; generation < generations; generation++) {
int i = 0;
while (population.chromosomes.size() < population.populationLimit) {
int i1 = rnd.nextInt(population.chromosomes.size());
int i2 = (i1 + 1 + rnd.nextInt(population.chromosomes.size() - 1)) % population.chromosomes.size();
Chromosome parent1 = population.chromosomes.get(i1);
Chromosome parent2 = population.chromosomes.get(i2);
int[][] pair = crossOver(parent1.p, parent2.p);
if (rnd.nextDouble() < mutationRate) {
mutate(pair[0]);
mutate(pair[1]);
}
population.chromosomes.add(new Chromosome(optimize(pair[0])));
population.chromosomes.add(new Chromosome(optimize(pair[1])));
}
population.nextGeneration();
bestState = population.chromosomes.get(0).p;
repaint();
}
}
int[][] crossOver(int[] p1, int[] p2) {
int n = p1.length;
int i1 = rnd.nextInt(n);
int i2 = (i1 + 1 + rnd.nextInt(n - 1)) % n;
int[] n1 = p1.clone();
int[] n2 = p2.clone();
boolean[] used1 = new boolean[n];
boolean[] used2 = new boolean[n];
for (int i = i1; ; i = (i + 1) % n) {
n1[i] = p2[i];
used1[n1[i]] = true;
n2[i] = p1[i];
used2[n2[i]] = true;
if (i == i2) {
break;
}
}
for (int i = (i2 + 1) % n; i != i1; i = (i + 1) % n) {
if (used1[n1[i]]) {
n1[i] = -1;
} else {
used1[n1[i]] = true;
}
if (used2[n2[i]]) {
n2[i] = -1;
} else {
used2[n2[i]] = true;
}
}
int pos1 = 0;
int pos2 = 0;
for (int i = 0; i < n; i++) {
if (n1[i] == -1) {
while (used1[pos1])
++pos1;
n1[i] = pos1++;
}
if (n2[i] == -1) {
while (used2[pos2])
++pos2;
n2[i] = pos2++;
}
}
return new int[][]{n1, n2};
}
void mutate(int[] p) {
int n = p.length;
int i = rnd.nextInt(n);
int j = (i + 1 + rnd.nextInt(n - 1)) % n;
reverse(p, i, j);
}
// http://en.wikipedia.org/wiki/2-opt
static void reverse(int[] p, int i, int j) {
int n = p.length;
// reverse order from i to j
while (i != j) {
int t = p[j];
p[j] = p[i];
p[i] = t;
i = (i + 1) % n;
if (i == j) break;
j = (j - 1 + n) % n;
}
}
double eval(int[] state) {
double res = 0;
for (int i = 0, j = state.length - 1; i < state.length; j = i++)
res += dist(x[state[i]], y[state[i]], x[state[j]], y[state[j]]);
return res;
}
static double dist(double x1, double y1, double x2, double y2) {
double dx = x1 - x2;
double dy = y1 - y2;
return Math.sqrt(dx * dx + dy * dy);
}
int[] getRandomPermutation(int n) {
int[] res = new int[n];
for (int i = 0; i < n; i++) {
int j = rnd.nextInt(i + 1);
res[i] = res[j];
res[j] = i;
}
return res;
}
// try all 2-opt moves
int[] optimize(int[] p) {
int[] res = p.clone();
for (boolean improved = true; improved; ) {
improved = false;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
if (i == j || (j + 1) % n == i) continue;
int i1 = (i - 1 + n) % n;
int j1 = (j + 1) % n;
double delta = dist(x[res[i1]], y[res[i1]], x[res[j]], y[res[j]])
+ dist(x[res[i]], y[res[i]], x[res[j1]], y[res[j1]])
- dist(x[res[i1]], y[res[i1]], x[res[i]], y[res[i]])
- dist(x[res[j]], y[res[j]], x[res[j1]], y[res[j1]]);
if (delta < -1e-9) {
reverse(res, i, j);
improved = true;
}
}
}
}
return res;
}
class Chromosome implements Comparable<Chromosome> {
final int[] p;
private double cost = Double.NaN;
public Chromosome(int[] p) {
this.p = p;
}
public double getCost() {
return Double.isNaN(cost) ? cost = eval(p) : cost;
}
@Override
public int compareTo(Chromosome o) {
return Double.compare(getCost(), o.getCost());
}
}
static class Population {
List<Chromosome> chromosomes = new ArrayList<>();
final int populationLimit;
public Population(int populationLimit) {
this.populationLimit = populationLimit;
}
public void nextGeneration() {
Collections.sort(chromosomes);
chromosomes = new ArrayList<>(chromosomes.subList(0, (chromosomes.size() + 1) / 2));
}
}
// visualization code
public GeneticAlgorithm() {
setContentPane(new JPanel() {
protected void paintComponent(Graphics g) {
super.paintComponent(g);
((Graphics2D) g).setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON);
((Graphics2D) g).setStroke(new BasicStroke(3));
g.setColor(Color.BLUE);
int w = getWidth() - 5;
int h = getHeight() - 30;
for (int i = 0, j = n - 1; i < n; j = i++)
g.drawLine((int) (x[bestState[i]] * w), (int) ((1 - y[bestState[i]]) * h),
(int) (x[bestState[j]] * w), (int) ((1 - y[bestState[j]]) * h));
g.setColor(Color.RED);
for (int i = 0; i < n; i++)
g.drawOval((int) (x[i] * w) - 1, (int) ((1 - y[i]) * h) - 1, 3, 3);
g.setColor(Color.BLACK);
g.drawString(String.format("length: %.3f", eval(bestState)), 5, h + 20);
g.drawString(String.format("generation: %d", generation), 150, h + 20);
}
});
setSize(new Dimension(600, 600));
setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
setVisible(true);
new Thread(this::geneticAlgorithm).start();
}
public static void main(String[] args) {
new GeneticAlgorithm();
}
}