1 |
|
/* |
2 |
< |
* Written by Bill Scherer and Doug Lea with assistance from members |
3 |
< |
* of JCP JSR-166 Expert Group and released to the public domain. Use, |
4 |
< |
* modify, and redistribute this code in any way without |
5 |
< |
* acknowledgement. |
2 |
> |
* Written by Doug Lea and Bill Scherer with assistance from members |
3 |
> |
* of JCP JSR-166 Expert Group and released to the public domain, as |
4 |
> |
* explained at http://creativecommons.org/licenses/publicdomain |
5 |
|
*/ |
6 |
|
|
7 |
+ |
import java.util.*; |
8 |
|
import java.util.concurrent.*; |
9 |
|
import java.util.concurrent.atomic.*; |
10 |
|
import java.util.concurrent.locks.*; |
11 |
|
|
12 |
+ |
/** |
13 |
+ |
* A parallel Traveling Salesperson Problem (TSP) program based on a |
14 |
+ |
* genetic algorithm using an Exchanger. A population of chromosomes |
15 |
+ |
* is distributed among "pools". The chromosomes represent tours, and |
16 |
+ |
* their fitness is the total tour length. Each chromosome is |
17 |
+ |
* initialized as a random tour. A Task is associated with each pool. |
18 |
+ |
* Each task repeatedly does, for a fixed number of iterations |
19 |
+ |
* (generations): |
20 |
+ |
* |
21 |
+ |
* <ol> |
22 |
+ |
* <li> Select a breeder b from the pool |
23 |
+ |
* <li> Create a strand of its tour with a random starting point and length |
24 |
+ |
* <li> Offer the strand to the exchanger, receiving a strand from |
25 |
+ |
* another pool |
26 |
+ |
* <li> Combine b and the received strand using crossing function to |
27 |
+ |
* create new chromosome c. |
28 |
+ |
* <li> Replace a chromosome in the pool with c. |
29 |
+ |
* </ol> |
30 |
+ |
* |
31 |
+ |
* See below for more details. |
32 |
+ |
* <p> |
33 |
+ |
* |
34 |
+ |
*/ |
35 |
|
public class TSPExchangerTest { |
36 |
< |
// Set SLS true to use as default the settings in Scherer, Lea, and |
37 |
< |
// Scott paper. Otherwise smaller values are used to speed up testing |
38 |
< |
static final boolean SLS = false; |
39 |
< |
|
40 |
< |
static final int DEFAULT_THREADS = SLS? 32: 8; |
41 |
< |
static final int DEFAULT_CITIES = SLS? 100: 50; |
42 |
< |
static final int DEFAULT_POPULATION = SLS? 1000: 500; |
43 |
< |
static final int DEFAULT_BREEDERS = SLS? 200: 100; |
44 |
< |
static final int DEFAULT_GENERATIONS = SLS? 20000: 10000; |
36 |
> |
static final int DEFAULT_MAX_THREADS = |
37 |
> |
(Runtime.getRuntime().availableProcessors() + 2); |
38 |
> |
|
39 |
> |
/** |
40 |
> |
* The problem size. Each city is a random point. The goal is to |
41 |
> |
* find a tour among them with smallest total Euclidean distance. |
42 |
> |
*/ |
43 |
> |
static final int DEFAULT_CITIES = 144; |
44 |
> |
|
45 |
> |
// Tuning parameters. |
46 |
> |
|
47 |
> |
/** |
48 |
> |
* The number of chromosomes per pool. Must be a power of two. |
49 |
> |
* |
50 |
> |
* Smaller values lead to faster iterations but poorer quality |
51 |
> |
* results |
52 |
> |
*/ |
53 |
> |
static final int DEFAULT_POOL_SIZE = 32; |
54 |
|
|
55 |
+ |
/** |
56 |
+ |
* The number of iterations per task. Convergence appears |
57 |
+ |
* to be roughly proportional to #cities-squared |
58 |
+ |
*/ |
59 |
+ |
static final int DEFAULT_GENERATIONS = DEFAULT_CITIES * DEFAULT_CITIES; |
60 |
+ |
|
61 |
+ |
/** |
62 |
+ |
* The number of pools. The total population is #pools * poolSize, |
63 |
+ |
* which should be roughly on the order of #cities-squared |
64 |
+ |
* |
65 |
+ |
* Smaller values lead to faster total runs but poorer quality |
66 |
+ |
* results |
67 |
+ |
*/ |
68 |
+ |
static final int DEFAULT_NPOOLS = DEFAULT_GENERATIONS / DEFAULT_POOL_SIZE; |
69 |
+ |
|
70 |
+ |
/** |
71 |
+ |
* The minimum length for a random chromosome strand. |
72 |
+ |
* Must be at least 1. |
73 |
+ |
*/ |
74 |
+ |
static final int MIN_STRAND_LENGTH = 3; |
75 |
+ |
|
76 |
+ |
/** |
77 |
+ |
* The probablility mask value for creating random strands, |
78 |
+ |
* that have lengths at least MIN_STRAND_LENGTH, and grow |
79 |
+ |
* with exposnential decay 2^(-(1/(RANDOM_STRAND_MASK + 1) |
80 |
+ |
* Must be 1 less than a power of two. |
81 |
+ |
*/ |
82 |
+ |
static final int RANDOM_STRAND_MASK = 7; |
83 |
+ |
|
84 |
+ |
/** |
85 |
+ |
* Probablility control for selecting breeders. |
86 |
+ |
* Breeders are selected starting at the best-fitness chromosome, |
87 |
+ |
* with exponentially decaying probablility |
88 |
+ |
* 1 / (poolSize >>> BREEDER_DECAY). |
89 |
+ |
* |
90 |
+ |
* Larger values usually cause faster convergence but poorer |
91 |
+ |
* quality results |
92 |
+ |
*/ |
93 |
+ |
static final int BREEDER_DECAY = 1; |
94 |
+ |
|
95 |
+ |
/** |
96 |
+ |
* Probablility control for selecting dyers. |
97 |
+ |
* Dyers are selected starting at the worst-fitness chromosome, |
98 |
+ |
* with exponentially decaying probablility |
99 |
+ |
* 1 / (poolSize >>> DYER_DECAY) |
100 |
+ |
* |
101 |
+ |
* Larger values usually cause faster convergence but poorer |
102 |
+ |
* quality results |
103 |
+ |
*/ |
104 |
+ |
static final int DYER_DECAY = 1; |
105 |
+ |
|
106 |
+ |
/** |
107 |
+ |
* The probability mask for a task to give up running and |
108 |
+ |
* resubmit itself. On each iteration, a task stops iterating |
109 |
+ |
* and resubmits itself with probability 1 / (RESUBMIT_MASK+1). |
110 |
+ |
* This avoids some tasks running to completion before others |
111 |
+ |
* even start when there are more pools than threads. |
112 |
+ |
* |
113 |
+ |
* Must be 1 less than a power of two. |
114 |
+ |
*/ |
115 |
+ |
static final int RESUBMIT_MASK = 63; |
116 |
+ |
|
117 |
+ |
static final boolean verbose = true; |
118 |
+ |
static final long SNAPSHOT_RATE = 10000; // in milliseconds |
119 |
+ |
|
120 |
+ |
/** |
121 |
+ |
* The set of cities. Created once per program run, to |
122 |
+ |
* make it easier to compare solutions across different runs. |
123 |
+ |
*/ |
124 |
+ |
static CitySet cities; |
125 |
|
|
126 |
|
public static void main(String[] args) throws Exception { |
127 |
< |
int maxThreads = DEFAULT_THREADS; |
127 |
> |
int maxThreads = DEFAULT_MAX_THREADS; |
128 |
|
int nCities = DEFAULT_CITIES; |
129 |
< |
int pSize = DEFAULT_POPULATION; |
130 |
< |
int nBreeders = DEFAULT_BREEDERS; |
131 |
< |
int numGenerations = DEFAULT_GENERATIONS; |
129 |
> |
int poolSize = DEFAULT_POOL_SIZE; |
130 |
> |
int nGen = nCities * nCities; |
131 |
> |
int nPools = nCities * nCities / poolSize; |
132 |
|
|
31 |
– |
// Parse and check args |
32 |
– |
int argc = 0; |
133 |
|
try { |
134 |
+ |
int argc = 0; |
135 |
|
while (argc < args.length) { |
136 |
|
String option = args[argc++]; |
137 |
< |
if (option.equals("-b")) |
37 |
< |
nBreeders = Integer.parseInt(args[argc]); |
38 |
< |
else if (option.equals("-c")) |
137 |
> |
if (option.equals("-c")) { |
138 |
|
nCities = Integer.parseInt(args[argc]); |
139 |
+ |
nGen = nCities * nCities; |
140 |
+ |
nPools = nCities * nCities / poolSize; |
141 |
+ |
} |
142 |
+ |
else if (option.equals("-p")) |
143 |
+ |
poolSize = Integer.parseInt(args[argc]); |
144 |
|
else if (option.equals("-g")) |
145 |
< |
numGenerations = Integer.parseInt(args[argc]); |
146 |
< |
else if (option.equals("-p")) |
147 |
< |
pSize = Integer.parseInt(args[argc]); |
148 |
< |
else |
145 |
> |
nGen = Integer.parseInt(args[argc]); |
146 |
> |
else if (option.equals("-n")) |
147 |
> |
nPools = Integer.parseInt(args[argc]); |
148 |
> |
else |
149 |
|
maxThreads = Integer.parseInt(option); |
150 |
|
argc++; |
151 |
|
} |
152 |
|
} |
49 |
– |
catch (NumberFormatException e) { |
50 |
– |
reportUsageErrorAndDie(); |
51 |
– |
System.exit(0); |
52 |
– |
} |
153 |
|
catch (Exception e) { |
154 |
|
reportUsageErrorAndDie(); |
155 |
|
} |
156 |
|
|
157 |
< |
// Display runtime parameters |
158 |
< |
System.out.print("TSPExchangerTest -b " + nBreeders); |
159 |
< |
System.out.print(" -c " + nCities); |
160 |
< |
System.out.print(" -g " + numGenerations); |
161 |
< |
System.out.print(" -p " + pSize); |
162 |
< |
System.out.print(" max threads " + maxThreads); |
163 |
< |
System.out.println(); |
64 |
< |
|
65 |
< |
// warmup |
66 |
< |
System.out.print("Threads: " + 2 + "\t"); |
67 |
< |
oneRun(2, |
68 |
< |
nCities, |
69 |
< |
pSize, |
70 |
< |
nBreeders, |
71 |
< |
numGenerations); |
72 |
< |
Thread.sleep(100); |
157 |
> |
System.out.print("TSPExchangerTest"); |
158 |
> |
System.out.print(" -c " + nCities); |
159 |
> |
System.out.print(" -g " + nGen); |
160 |
> |
System.out.print(" -p " + poolSize); |
161 |
> |
System.out.print(" -n " + nPools); |
162 |
> |
System.out.print(" max threads " + maxThreads); |
163 |
> |
System.out.println(); |
164 |
|
|
165 |
< |
int k = 4; |
166 |
< |
for (int i = 2; i <= maxThreads;) { |
167 |
< |
System.out.print("Threads: " + i + "\t"); |
168 |
< |
oneRun(i, |
78 |
< |
nCities, |
79 |
< |
pSize, |
80 |
< |
nBreeders, |
81 |
< |
numGenerations); |
82 |
< |
Thread.sleep(100); |
83 |
< |
if (i == k) { |
84 |
< |
k = i << 1; |
85 |
< |
i = i + (i >>> 1); |
86 |
< |
} |
87 |
< |
else |
88 |
< |
i = k; |
89 |
< |
} |
165 |
> |
cities = new CitySet(nCities); |
166 |
> |
|
167 |
> |
for (int i = 2; i <= maxThreads; i += 2) |
168 |
> |
oneRun(i, nPools, poolSize, nGen); |
169 |
|
} |
170 |
|
|
171 |
< |
private static void reportUsageErrorAndDie() { |
172 |
< |
System.out.print("usage: TSPExchangerTest [-b #breeders] [-c #cities]"); |
173 |
< |
System.out.println(" [-g #generations]"); |
174 |
< |
System.out.println(" [-p population size] [ #threads]"); |
171 |
> |
static void reportUsageErrorAndDie() { |
172 |
> |
System.out.print("usage: TSPExchangerTest"); |
173 |
> |
System.out.print(" [-c #cities]"); |
174 |
> |
System.out.print(" [-p #poolSize]"); |
175 |
> |
System.out.print(" [-g #generations]"); |
176 |
> |
System.out.print(" [-n #pools]"); |
177 |
> |
System.out.print(" #threads]"); |
178 |
> |
System.out.println(); |
179 |
|
System.exit(0); |
180 |
|
} |
181 |
|
|
182 |
< |
static void oneRun(int nThreads, |
183 |
< |
int nCities, |
184 |
< |
int pSize, |
185 |
< |
int nBreeders, |
186 |
< |
int numGenerations) |
187 |
< |
throws Exception { |
188 |
< |
CyclicBarrier runBarrier = new CyclicBarrier(nThreads + 1); |
189 |
< |
Population p = new Population(nCities, pSize, nBreeders, nThreads, |
190 |
< |
numGenerations, runBarrier); |
191 |
< |
|
192 |
< |
// Run the test |
193 |
< |
long startTime = System.currentTimeMillis(); |
194 |
< |
runBarrier.await(); // start 'em off |
195 |
< |
runBarrier.await(); // wait 'til they're done |
196 |
< |
long stopTime = System.currentTimeMillis(); |
197 |
< |
long elapsed = stopTime - startTime; |
198 |
< |
long rate = (numGenerations * 1000) / elapsed; |
199 |
< |
double secs = (double)elapsed / 1000.0; |
182 |
> |
/** |
183 |
> |
* Perform one run with the given parameters. Each run completes |
184 |
> |
* when all but one of the tasks has finished. The last remaining |
185 |
> |
* task may have no one left to exchange with, so the pool is |
186 |
> |
* abruptly terminated. |
187 |
> |
*/ |
188 |
> |
static void oneRun(int nThreads, int nPools, int poolSize, int nGen) |
189 |
> |
throws InterruptedException { |
190 |
> |
Population p = new Population(nThreads, nPools, poolSize, nGen); |
191 |
> |
ProgressMonitor mon = null; |
192 |
> |
if (verbose) { |
193 |
> |
mon = new ProgressMonitor(p); |
194 |
> |
mon.start(); |
195 |
> |
} |
196 |
> |
p.printSnapshot(0); |
197 |
> |
long startTime = System.nanoTime(); |
198 |
> |
p.start(); |
199 |
> |
p.awaitTasks(); |
200 |
> |
long stopTime = System.nanoTime(); |
201 |
> |
if (mon != null) |
202 |
> |
mon.interrupt(); |
203 |
> |
p.shutdown(); |
204 |
> |
Thread.sleep(100); |
205 |
|
|
206 |
< |
// Display results |
207 |
< |
System.out.print(LoopHelpers.rightJustify((int)p.bestFitness()) + |
208 |
< |
" fitness"); |
209 |
< |
System.out.print(LoopHelpers.rightJustify(rate) + " gen/s \t"); |
210 |
< |
System.out.print(secs + "s elapsed"); |
123 |
< |
System.out.println(); |
206 |
> |
long elapsed = stopTime - startTime; |
207 |
> |
long rate = elapsed / (nPools * nGen); |
208 |
> |
double secs = (double)elapsed / 1000000000.0; |
209 |
> |
p.printSnapshot(secs); |
210 |
> |
System.out.printf("%10d ns per transfer\n", rate); |
211 |
|
} |
212 |
|
|
213 |
+ |
|
214 |
+ |
/** |
215 |
+ |
* A Population creates the pools, tasks, and threads for a run |
216 |
+ |
* and has control methods to start, stop, and report progress. |
217 |
+ |
*/ |
218 |
|
static final class Population { |
219 |
< |
final Chromosome[] individuals; |
220 |
< |
final Exchanger<Chromosome> x; |
221 |
< |
final CitySet cities; |
222 |
< |
final int[] dyers; |
223 |
< |
final int[] breeders; |
224 |
< |
final CyclicBarrier generationBarrier; |
133 |
< |
final Thread[] threads; |
134 |
< |
final boolean[] doneMating; |
135 |
< |
final ReentrantLock matingBarrierLock; |
136 |
< |
final Condition matingBarrier; |
137 |
< |
final LoopHelpers.SimpleRandom[] rngs; |
219 |
> |
final Task[] tasks; |
220 |
> |
final Exchanger<Strand> exchanger; |
221 |
> |
final ThreadPoolExecutor exec; |
222 |
> |
final CountDownLatch done; |
223 |
> |
final int nGen; |
224 |
> |
final int poolSize; |
225 |
|
final int nThreads; |
139 |
– |
volatile int matingBarrierCount; |
226 |
|
|
227 |
< |
// action to run between each generation |
142 |
< |
class BarrierAction implements Runnable { |
143 |
< |
public void run() { |
144 |
< |
prepareToBreed(); |
145 |
< |
resetMatingBarrier(); |
146 |
< |
} |
147 |
< |
} |
148 |
< |
|
149 |
< |
Population(int nCities, |
150 |
< |
int pSize, |
151 |
< |
int nBreeders, |
152 |
< |
int nThreads, |
153 |
< |
int nGen, |
154 |
< |
CyclicBarrier runBarrier) { |
227 |
> |
Population(int nThreads, int nPools, int poolSize, int nGen) { |
228 |
|
this.nThreads = nThreads; |
229 |
< |
// rngs[nThreads] is for global actions; others are per-thread |
230 |
< |
this.rngs = new LoopHelpers.SimpleRandom[nThreads+1]; |
231 |
< |
for (int i = 0; i < rngs.length; ++i) |
232 |
< |
rngs[i] = new LoopHelpers.SimpleRandom(); |
233 |
< |
this.cities = new CitySet(nCities, rngs[nThreads]); |
234 |
< |
this.individuals = new Chromosome[pSize]; |
235 |
< |
for (int i = 0; i < individuals.length; i++) |
236 |
< |
individuals[i] = new Chromosome(cities, nCities, |
237 |
< |
rngs[nThreads]); |
238 |
< |
this.doneMating = new boolean[nThreads]; |
239 |
< |
this.dyers = new int[nBreeders]; |
240 |
< |
this.breeders = new int[nBreeders]; |
241 |
< |
|
242 |
< |
this.x = new Exchanger(); |
243 |
< |
|
244 |
< |
this.matingBarrierLock = new ReentrantLock(); |
245 |
< |
this.matingBarrier = matingBarrierLock.newCondition(); |
246 |
< |
|
247 |
< |
BarrierAction ba = new BarrierAction(); |
248 |
< |
this.generationBarrier = new CyclicBarrier(nThreads, ba); |
249 |
< |
ba.run(); // prepare for first generation |
250 |
< |
|
251 |
< |
this.threads = new Thread[nThreads]; |
252 |
< |
for (int i = 0; i < nThreads; i++) { |
253 |
< |
Runner r = new Runner(i, this, runBarrier, nGen); |
254 |
< |
threads[i] = new Thread(r); |
255 |
< |
threads[i].start(); |
229 |
> |
this.nGen = nGen; |
230 |
> |
this.poolSize = poolSize; |
231 |
> |
this.exchanger = new Exchanger<Strand>(); |
232 |
> |
this.done = new CountDownLatch(nPools-1); |
233 |
> |
this.tasks = new Task[nPools]; |
234 |
> |
for (int i = 0; i < nPools; i++) |
235 |
> |
tasks[i] = new Task(this); |
236 |
> |
BlockingQueue<Runnable> tq = |
237 |
> |
new LinkedBlockingQueue<Runnable>(); |
238 |
> |
this.exec = new ThreadPoolExecutor(nThreads, nThreads, |
239 |
> |
0L, TimeUnit.MILLISECONDS, |
240 |
> |
tq); |
241 |
> |
exec.prestartAllCoreThreads(); |
242 |
> |
} |
243 |
> |
|
244 |
> |
/** Start the tasks */ |
245 |
> |
void start() { |
246 |
> |
for (int i = 0; i < tasks.length; i++) |
247 |
> |
exec.execute(tasks[i]); |
248 |
> |
} |
249 |
> |
|
250 |
> |
/** Stop the tasks */ |
251 |
> |
void shutdown() { |
252 |
> |
exec.shutdownNow(); |
253 |
> |
} |
254 |
> |
|
255 |
> |
/** Called by task upon terminations */ |
256 |
> |
void taskDone() { |
257 |
> |
done.countDown(); |
258 |
> |
} |
259 |
> |
|
260 |
> |
/** Wait for (all but one) task to complete */ |
261 |
> |
void awaitTasks() throws InterruptedException { |
262 |
> |
done.await(); |
263 |
> |
} |
264 |
> |
|
265 |
> |
/** |
266 |
> |
* Called by a task to resubmit itself after completing |
267 |
> |
* fewer than nGen iterations. |
268 |
> |
*/ |
269 |
> |
void resubmit(Task task) { |
270 |
> |
exec.execute(task); |
271 |
> |
} |
272 |
> |
|
273 |
> |
void printSnapshot(double secs) { |
274 |
> |
int gens = 0; |
275 |
> |
double best = Double.MAX_VALUE; |
276 |
> |
double worst = 0; |
277 |
> |
for (int k = 0; k < tasks.length; ++k) { |
278 |
> |
gens += tasks[k].gen; |
279 |
> |
Chromosome[] cs = tasks[k].chromosomes; |
280 |
> |
float b = cs[0].fitness; |
281 |
> |
if (b < best) |
282 |
> |
best = b; |
283 |
> |
float w = cs[cs.length-1].fitness; |
284 |
> |
if (w > worst) |
285 |
> |
worst = w; |
286 |
> |
} |
287 |
> |
int avegen = (done.getCount() == 0)? nGen : gens / tasks.length; |
288 |
> |
System.out.printf("Time:%9.3f Best:%9.3f Worst:%9.3f Gen:%6d Threads:%4d\n", |
289 |
> |
secs, best, worst, avegen, nThreads); |
290 |
> |
} |
291 |
> |
|
292 |
> |
float averageFitness() { // currently unused |
293 |
> |
float total = 0; |
294 |
> |
int count = 0; |
295 |
> |
for (int k = 0; k < tasks.length; ++k) { |
296 |
> |
Chromosome[] cs = tasks[k].chromosomes; |
297 |
> |
for (int i = 0; i < cs.length; i++) |
298 |
> |
total += cs[i].fitness; |
299 |
> |
count += cs.length; |
300 |
|
} |
301 |
+ |
return total/(float)count; |
302 |
|
} |
303 |
< |
|
186 |
< |
double averageFitness() { |
187 |
< |
double total = 0; |
188 |
< |
for (int i = 0; i < individuals.length; i++) |
189 |
< |
total += individuals[i].fitness; |
190 |
< |
return total/(double)individuals.length; |
191 |
< |
} |
192 |
< |
|
193 |
< |
double bestFitness() { |
194 |
< |
double best = individuals[0].fitness; |
195 |
< |
for (int i = 0; i < individuals.length; i++) |
196 |
< |
if (individuals[i].fitness < best) |
197 |
< |
best = individuals[i].fitness; |
198 |
< |
return best; |
199 |
< |
} |
303 |
> |
} |
304 |
|
|
305 |
< |
void resetMatingBarrier() { |
306 |
< |
matingBarrierCount = nThreads - 1; |
307 |
< |
} |
305 |
> |
/** |
306 |
> |
* A Task updates its pool of chromosomes.. |
307 |
> |
*/ |
308 |
> |
static final class Task implements Runnable { |
309 |
> |
/** The pool of chromosomes, kept in sorted order */ |
310 |
> |
final Chromosome[] chromosomes; |
311 |
> |
final Population pop; |
312 |
> |
/** The common exchanger, same for all tasks */ |
313 |
> |
final Exchanger<Strand> exchanger; |
314 |
> |
/** The current strand being exchanged */ |
315 |
> |
Strand strand; |
316 |
> |
/** Bitset used in cross */ |
317 |
> |
final int[] inTour; |
318 |
> |
final RNG rng; |
319 |
> |
final int poolSize; |
320 |
> |
final int nGen; |
321 |
> |
int gen; |
322 |
|
|
323 |
< |
void awaitMatingBarrier(int tid) { |
324 |
< |
doneMating[tid] = true; // heuristically set before lock |
325 |
< |
matingBarrierLock.lock(); |
323 |
> |
Task(Population pop) { |
324 |
> |
this.pop = pop; |
325 |
> |
this.nGen = pop.nGen; |
326 |
> |
this.gen = 0; |
327 |
> |
this.poolSize = pop.poolSize; |
328 |
> |
this.exchanger = pop.exchanger; |
329 |
> |
this.rng = new RNG(); |
330 |
> |
int length = cities.length; |
331 |
> |
this.strand = new Strand(length); |
332 |
> |
this.inTour = new int[(length >>> 5) + 1]; |
333 |
> |
this.chromosomes = new Chromosome[poolSize]; |
334 |
> |
for (int j = 0; j < poolSize; ++j) |
335 |
> |
chromosomes[j] = new Chromosome(length, rng); |
336 |
> |
Arrays.sort(chromosomes); |
337 |
> |
} |
338 |
> |
|
339 |
> |
/** |
340 |
> |
* Run one or more update cycles. |
341 |
> |
*/ |
342 |
> |
public void run() { |
343 |
|
try { |
344 |
< |
int m = matingBarrierCount--; |
345 |
< |
if (m < 1) { |
346 |
< |
for (int i = 0; i < doneMating.length; ++i) |
347 |
< |
doneMating[i] = false; |
348 |
< |
Thread.interrupted(); // clear |
214 |
< |
matingBarrier.signalAll(); |
215 |
< |
} else { |
216 |
< |
doneMating[tid] = true; |
217 |
< |
if (m == 1 && nThreads > 2) { |
218 |
< |
for (int j = 0; j < doneMating.length; ++j) { |
219 |
< |
if (!doneMating[j]) { |
220 |
< |
threads[j].interrupt(); |
221 |
< |
break; |
222 |
< |
} |
223 |
< |
} |
344 |
> |
for (;;) { |
345 |
> |
update(); |
346 |
> |
if (++gen >= nGen) { |
347 |
> |
pop.taskDone(); |
348 |
> |
return; |
349 |
|
} |
350 |
< |
try { |
351 |
< |
do { |
352 |
< |
matingBarrier.await(); |
353 |
< |
} while (matingBarrierCount >= 0); |
229 |
< |
} catch(InterruptedException ie) {} |
350 |
> |
if ((rng.next() & RESUBMIT_MASK) == 1) { |
351 |
> |
pop.resubmit(this); |
352 |
> |
return; |
353 |
> |
} |
354 |
|
} |
355 |
< |
} finally { |
356 |
< |
matingBarrierLock.unlock(); |
355 |
> |
} catch (InterruptedException ie) { |
356 |
> |
pop.taskDone(); |
357 |
|
} |
358 |
|
} |
359 |
|
|
360 |
< |
void prepareToBreed() { |
361 |
< |
|
362 |
< |
// Calculate statistics |
363 |
< |
double totalFitness = 0; |
364 |
< |
double worstFitness = 0; |
365 |
< |
double bestFitness = individuals[0].fitness; |
366 |
< |
|
367 |
< |
for (int i = 0; i < individuals.length; i++) { |
368 |
< |
totalFitness += individuals[i].fitness; |
369 |
< |
if (individuals[i].fitness > worstFitness) |
370 |
< |
worstFitness = individuals[i].fitness; |
371 |
< |
if (individuals[i].fitness < bestFitness) |
372 |
< |
bestFitness = individuals[i].fitness; |
373 |
< |
} |
374 |
< |
|
375 |
< |
double[] lifeNorm = new double[individuals.length]; |
376 |
< |
double lifeNormTotal = 0; |
377 |
< |
double[] deathNorm = new double[individuals.length]; |
378 |
< |
double deathNormTotal = 0; |
379 |
< |
for (int i = 0; i < individuals.length; i++) { |
380 |
< |
deathNorm[i] = (individuals[i].fitness - bestFitness) |
381 |
< |
/ (worstFitness - bestFitness + 1) + .05; |
382 |
< |
deathNorm[i] = (deathNorm[i] * deathNorm[i]); |
383 |
< |
lifeNorm[i] = 1.0 - deathNorm[i]; |
384 |
< |
lifeNormTotal += lifeNorm[i]; |
385 |
< |
deathNormTotal += deathNorm[i]; |
386 |
< |
} |
387 |
< |
|
388 |
< |
double deathScale = deathNormTotal / (double)0x7FFFFFFF; |
389 |
< |
double lifeScale = lifeNormTotal / (double)0x7FFFFFFF; |
390 |
< |
|
391 |
< |
int nSub = breeders.length / nThreads; |
392 |
< |
LoopHelpers.SimpleRandom random = rngs[nThreads]; |
393 |
< |
|
394 |
< |
// Select breeders (need to be distinct) |
395 |
< |
for (int i = 0; i < nSub; i++) { |
396 |
< |
boolean newBreeder; |
397 |
< |
int lucky; |
398 |
< |
do { |
399 |
< |
newBreeder = true; |
400 |
< |
double choice = lifeScale * (double)random.next(); |
401 |
< |
for (lucky = 0; lucky < individuals.length; lucky++) { |
402 |
< |
choice -= lifeNorm[lucky]; |
403 |
< |
if (choice <= 0) |
404 |
< |
break; |
405 |
< |
} |
406 |
< |
for (int j = 0; j < i; j++) |
407 |
< |
if (breeders[j] == lucky) |
408 |
< |
newBreeder = false; |
409 |
< |
} while (!newBreeder); |
410 |
< |
breeders[i] = lucky; |
411 |
< |
} |
412 |
< |
|
413 |
< |
// Select dead guys (need to be distinct) |
414 |
< |
for (int i = 0; i < nSub; i++) { |
415 |
< |
boolean newDead; |
416 |
< |
int victim; |
417 |
< |
do { |
418 |
< |
newDead = true; |
419 |
< |
double choice = deathScale * (double)random.next(); |
420 |
< |
for (victim = 0; victim < individuals.length; victim++) { |
421 |
< |
choice -= deathNorm[victim]; |
422 |
< |
if (choice <= 0) |
423 |
< |
break; |
424 |
< |
} |
425 |
< |
for (int j = 0; j < i; j++) |
426 |
< |
if (dyers[j] == victim) |
427 |
< |
newDead = false; |
428 |
< |
} while (!newDead); |
429 |
< |
dyers[i] = victim; |
430 |
< |
} |
360 |
> |
/** |
361 |
> |
* Choose a breeder, exchange strand with another pool, and |
362 |
> |
* cross them to create new chromosome to replace a chosen |
363 |
> |
* dyer. |
364 |
> |
*/ |
365 |
> |
void update() throws InterruptedException { |
366 |
> |
int b = chooseBreeder(); |
367 |
> |
int d = chooseDyer(b); |
368 |
> |
Chromosome breeder = chromosomes[b]; |
369 |
> |
Chromosome child = chromosomes[d]; |
370 |
> |
chooseStrand(breeder); |
371 |
> |
strand = exchanger.exchange(strand); |
372 |
> |
cross(breeder, child); |
373 |
> |
fixOrder(child, d); |
374 |
> |
} |
375 |
> |
|
376 |
> |
/** |
377 |
> |
* Choose a breeder, with exponentially decreasing probability |
378 |
> |
* starting at best. |
379 |
> |
* @return index of selected breeder |
380 |
> |
*/ |
381 |
> |
int chooseBreeder() { |
382 |
> |
int mask = (poolSize >>> BREEDER_DECAY) - 1; |
383 |
> |
int b = 0; |
384 |
> |
while ((rng.next() & mask) != mask) { |
385 |
> |
if (++b >= poolSize) |
386 |
> |
b = 0; |
387 |
> |
} |
388 |
> |
return b; |
389 |
> |
} |
390 |
> |
|
391 |
> |
/** |
392 |
> |
* Choose a chromosome that will be replaced, with |
393 |
> |
* exponentially decreasing probablility starting at |
394 |
> |
* worst, ignoring the excluded index |
395 |
> |
* @param exclude index to ignore; use -1 to not exclude any |
396 |
> |
* @return index of selected dyer |
397 |
> |
*/ |
398 |
> |
int chooseDyer(int exclude) { |
399 |
> |
int mask = (poolSize >>> DYER_DECAY) - 1; |
400 |
> |
int d = poolSize - 1; |
401 |
> |
while (d == exclude || (rng.next() & mask) != mask) { |
402 |
> |
if (--d < 0) |
403 |
> |
d = poolSize - 1; |
404 |
> |
} |
405 |
> |
return d; |
406 |
> |
} |
407 |
> |
|
408 |
> |
/** |
409 |
> |
* Select a random strand of b's. |
410 |
> |
* @param breeder the breeder |
411 |
> |
*/ |
412 |
> |
void chooseStrand(Chromosome breeder) { |
413 |
> |
int[] bs = breeder.alleles; |
414 |
> |
int length = bs.length; |
415 |
> |
int strandLength = MIN_STRAND_LENGTH; |
416 |
> |
while (strandLength < length && |
417 |
> |
(rng.next() & RANDOM_STRAND_MASK) != RANDOM_STRAND_MASK) |
418 |
> |
strandLength++; |
419 |
> |
strand.strandLength = strandLength; |
420 |
> |
int[] ss = strand.alleles; |
421 |
> |
int k = (rng.next() & 0x7FFFFFFF) % length; |
422 |
> |
for (int i = 0; i < strandLength; ++i) { |
423 |
> |
ss[i] = bs[k]; |
424 |
> |
if (++k >= length) k = 0; |
425 |
> |
} |
426 |
> |
} |
427 |
> |
|
428 |
> |
/** |
429 |
> |
* Copy current strand to start of c's, and then append all |
430 |
> |
* remaining b's that aren't in the strand. |
431 |
> |
* @param breeder the breeder |
432 |
> |
* @param child the child |
433 |
> |
*/ |
434 |
> |
void cross(Chromosome breeder, Chromosome child) { |
435 |
> |
for (int k = 0; k < inTour.length; ++k) // clear bitset |
436 |
> |
inTour[k] = 0; |
437 |
> |
|
438 |
> |
// Copy current strand to c |
439 |
> |
int[] cs = child.alleles; |
440 |
> |
int ssize = strand.strandLength; |
441 |
> |
int[] ss = strand.alleles; |
442 |
> |
int i; |
443 |
> |
for (i = 0; i < ssize; ++i) { |
444 |
> |
int x = ss[i]; |
445 |
> |
cs[i] = x; |
446 |
> |
inTour[x >>> 5] |= 1 << (x & 31); // record in bit set |
447 |
> |
} |
448 |
> |
|
449 |
> |
// Find index of matching origin in b |
450 |
> |
int first = cs[0]; |
451 |
> |
int j = 0; |
452 |
> |
int[] bs = breeder.alleles; |
453 |
> |
while (bs[j] != first) |
454 |
> |
++j; |
455 |
> |
|
456 |
> |
// Append remaining b's that aren't already in tour |
457 |
> |
while (i < cs.length) { |
458 |
> |
if (++j >= bs.length) j = 0; |
459 |
> |
int x = bs[j]; |
460 |
> |
if ((inTour[x >>> 5] & (1 << (x & 31))) == 0) |
461 |
> |
cs[i++] = x; |
462 |
> |
} |
463 |
|
|
464 |
|
} |
465 |
|
|
466 |
< |
|
467 |
< |
void nextGeneration(int tid, int matings) |
468 |
< |
throws InterruptedException, BrokenBarrierException { |
469 |
< |
|
470 |
< |
int firstChild = ((individuals.length * tid) / nThreads); |
471 |
< |
int lastChild = ((individuals.length * (tid + 1)) / nThreads); |
472 |
< |
int nChildren = lastChild - firstChild; |
473 |
< |
|
474 |
< |
int firstSub = ((breeders.length * tid) / nThreads); |
475 |
< |
int lastSub = ((breeders.length * (tid + 1)) / nThreads); |
476 |
< |
int nSub = lastSub - firstSub; |
477 |
< |
|
478 |
< |
Chromosome[] children = new Chromosome[nChildren]; |
479 |
< |
|
480 |
< |
LoopHelpers.SimpleRandom random = rngs[tid]; |
481 |
< |
|
326 |
< |
for (int i = 0; i < nSub; i++) { |
327 |
< |
Chromosome parent = individuals[breeders[firstSub + i]]; |
328 |
< |
Chromosome offspring = new Chromosome(parent); |
329 |
< |
int k = 0; |
330 |
< |
while (k < matings && matingBarrierCount > 0) { |
331 |
< |
try { |
332 |
< |
Chromosome other = x.exchange(offspring); |
333 |
< |
offspring = offspring.reproduceWith(other, random); |
334 |
< |
++k; |
335 |
< |
} catch (InterruptedException to) { |
336 |
< |
break; |
337 |
< |
} |
466 |
> |
/** |
467 |
> |
* Fix the sort order of a changed Chromosome c at position k |
468 |
> |
* @param c the chromosome |
469 |
> |
* @param k the index |
470 |
> |
*/ |
471 |
> |
void fixOrder(Chromosome c, int k) { |
472 |
> |
Chromosome[] cs = chromosomes; |
473 |
> |
float oldFitness = c.fitness; |
474 |
> |
c.recalcFitness(); |
475 |
> |
float newFitness = c.fitness; |
476 |
> |
if (newFitness < oldFitness) { |
477 |
> |
int j = k; |
478 |
> |
int p = j - 1; |
479 |
> |
while (p >= 0 && cs[p].fitness > newFitness) { |
480 |
> |
cs[j] = cs[p]; |
481 |
> |
j = p--; |
482 |
|
} |
483 |
< |
if (k != 0) |
484 |
< |
children[i] = offspring; |
485 |
< |
else { |
486 |
< |
// No peers, so we mate with ourselves |
487 |
< |
for ( ; i < nSub - 1; i += 2) { |
488 |
< |
int cur = firstSub + i; |
489 |
< |
Chromosome bro = individuals[breeders[cur]]; |
346 |
< |
Chromosome sis = individuals[breeders[cur + 1]]; |
347 |
< |
|
348 |
< |
children[i] = bro.breedWith(sis, matings, random); |
349 |
< |
children[i+1] = sis.breedWith(bro, matings, random); |
350 |
< |
} |
351 |
< |
|
352 |
< |
// Not even a sibling, so we go to asexual reproduction |
353 |
< |
if (i < nSub) |
354 |
< |
children[i] = individuals[breeders[firstSub + 1]]; |
355 |
< |
break; |
483 |
> |
cs[j] = c; |
484 |
> |
} else if (newFitness > oldFitness) { |
485 |
> |
int j = k; |
486 |
> |
int n = j + 1; |
487 |
> |
while (n < cs.length && cs[n].fitness < newFitness) { |
488 |
> |
cs[j] = cs[n]; |
489 |
> |
j = n++; |
490 |
|
} |
491 |
< |
|
358 |
< |
} |
359 |
< |
|
360 |
< |
awaitMatingBarrier(tid); |
361 |
< |
|
362 |
< |
// Kill off dead guys |
363 |
< |
for (int i = 0; i < nSub; i++) { |
364 |
< |
individuals[dyers[firstSub + 1]] = children[i]; |
491 |
> |
cs[j] = c; |
492 |
|
} |
366 |
– |
|
367 |
– |
generationBarrier.await(); |
493 |
|
} |
494 |
|
} |
495 |
|
|
496 |
< |
static final class Chromosome { |
497 |
< |
private final CitySet cities; |
498 |
< |
private final int[] alleles; |
499 |
< |
private final int length; |
500 |
< |
public double fitness; // immutable after publication |
501 |
< |
|
502 |
< |
// Basic constructor - gets randomized genetic code |
503 |
< |
Chromosome(CitySet cities, int length, |
504 |
< |
LoopHelpers.SimpleRandom random) { |
505 |
< |
this.length = length; |
506 |
< |
this.cities = cities; |
507 |
< |
// Initialize alleles to a random shuffle |
496 |
> |
/** |
497 |
> |
* A Chromosome is a candidate TSP tour. |
498 |
> |
*/ |
499 |
> |
static final class Chromosome implements Comparable { |
500 |
> |
/** Index of cities in tour order */ |
501 |
> |
final int[] alleles; |
502 |
> |
/** Total tour length */ |
503 |
> |
float fitness; |
504 |
> |
|
505 |
> |
/** |
506 |
> |
* Initialize to random tour |
507 |
> |
*/ |
508 |
> |
Chromosome(int length, RNG random) { |
509 |
|
alleles = new int[length]; |
510 |
|
for (int i = 0; i < length; i++) |
511 |
|
alleles[i] = i; |
512 |
|
for (int i = length - 1; i > 0; i--) { |
513 |
+ |
int idx = (random.next() & 0x7FFFFFFF) % alleles.length; |
514 |
|
int tmp = alleles[i]; |
388 |
– |
int idx = random.next() % length; |
515 |
|
alleles[i] = alleles[idx]; |
516 |
|
alleles[idx] = tmp; |
517 |
|
} |
518 |
|
recalcFitness(); |
519 |
|
} |
520 |
< |
|
521 |
< |
// Copy constructor - clones parent's genetic code |
522 |
< |
Chromosome(Chromosome clone) { |
523 |
< |
length = clone.length; |
524 |
< |
cities = clone.cities; |
399 |
< |
fitness = clone.fitness; |
400 |
< |
alleles = new int[length]; |
401 |
< |
System.arraycopy(clone.alleles, 0, alleles, 0, length); |
402 |
< |
} |
403 |
< |
|
404 |
< |
int getAllele(int offset) { |
405 |
< |
return alleles[offset % length]; |
406 |
< |
} |
407 |
< |
void setAllele(int offset, int v) { |
408 |
< |
alleles[offset % length] = v; |
520 |
> |
|
521 |
> |
public int compareTo(Object x) { // to enable sorting |
522 |
> |
float xf = ((Chromosome)x).fitness; |
523 |
> |
float f = fitness; |
524 |
> |
return ((f == xf)? 0 :((f < xf)? -1 : 1)); |
525 |
|
} |
526 |
< |
|
526 |
> |
|
527 |
|
void recalcFitness() { |
528 |
< |
fitness = cities.distanceBetween(alleles[0], alleles[length-1]); |
529 |
< |
for (int i = 1; i < length; i++) { |
530 |
< |
fitness += cities.distanceBetween(alleles[i-1], alleles[i]); |
531 |
< |
} |
532 |
< |
} |
533 |
< |
|
534 |
< |
Chromosome breedWith(Chromosome partner, int n, |
535 |
< |
LoopHelpers.SimpleRandom random) { |
536 |
< |
Chromosome offspring = new Chromosome(this); |
537 |
< |
for (int i = 0; i < n; i++) |
538 |
< |
offspring = offspring.reproduceWith(partner, random); |
539 |
< |
return offspring; |
528 |
> |
int[] a = alleles; |
529 |
> |
int len = a.length; |
530 |
> |
int p = a[0]; |
531 |
> |
float f = cities.distanceBetween(a[len-1], p); |
532 |
> |
for (int i = 1; i < len; i++) { |
533 |
> |
int n = a[i]; |
534 |
> |
f += cities.distanceBetween(p, n); |
535 |
> |
p = n; |
536 |
> |
} |
537 |
> |
fitness = f; |
538 |
> |
} |
539 |
> |
|
540 |
> |
void validate() { // Ensure that this is a valid tour. |
541 |
> |
int len = alleles.length; |
542 |
> |
boolean[] used = new boolean[len]; |
543 |
> |
for (int i = 0; i < len; ++i) |
544 |
> |
used[alleles[i]] = true; |
545 |
> |
for (int i = 0; i < len; ++i) |
546 |
> |
if (!used[i]) |
547 |
> |
throw new Error("Bad tour"); |
548 |
|
} |
549 |
< |
|
426 |
< |
Chromosome reproduceWith(Chromosome other, |
427 |
< |
LoopHelpers.SimpleRandom random) { |
428 |
< |
Chromosome child = new Chromosome(this); |
429 |
< |
int coStart = random.next() % length; |
430 |
< |
int coLen = 3; |
431 |
< |
while (1 == (random.next() & 1) && (coLen < length)) |
432 |
< |
coLen++; |
433 |
< |
int cPos, pPos; |
434 |
< |
|
435 |
< |
int join = other.getAllele(coStart); |
436 |
< |
child.alleles[0] = join; |
437 |
< |
|
438 |
< |
for (pPos = 0; alleles[pPos] != join; pPos++) |
439 |
< |
; |
440 |
< |
|
441 |
< |
for (cPos = 1; cPos < coLen; cPos++) |
442 |
< |
child.setAllele(cPos, other.getAllele(coStart + cPos)); |
443 |
< |
|
444 |
< |
for (int i = 0; i < length; i++) { |
445 |
< |
boolean found = false; |
446 |
< |
int allele = getAllele(pPos++); |
447 |
< |
for (int j = 0; j < coLen; j++) { |
448 |
< |
if (found = (child.getAllele(j) == allele)) |
449 |
< |
break; |
450 |
< |
} |
451 |
< |
if (!found) |
452 |
< |
child.setAllele(cPos++, allele); |
453 |
< |
} |
454 |
< |
|
455 |
< |
child.recalcFitness(); |
456 |
< |
return child; |
457 |
< |
} |
458 |
< |
|
549 |
> |
|
550 |
|
} |
551 |
+ |
|
552 |
|
/** |
553 |
< |
* A collection of (x,y) points that represent cities |
553 |
> |
* A Strand is a random sub-sequence of a Chromosome. Each task |
554 |
> |
* creates only one strand, and then trades it with others, |
555 |
> |
* refilling it on each iteration. |
556 |
> |
*/ |
557 |
> |
static final class Strand { |
558 |
> |
final int[] alleles; |
559 |
> |
int strandLength; |
560 |
> |
Strand(int length) { alleles = new int[length]; } |
561 |
> |
} |
562 |
> |
|
563 |
> |
/** |
564 |
> |
* A collection of (x,y) points that represent cities. Distances |
565 |
> |
* are scaled in [0,1) to simply checking results. The expected |
566 |
> |
* optimal TSP for random points is believed to be around 0.76 * |
567 |
> |
* sqrt(N). For papers discussing this, see |
568 |
> |
* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html for |
569 |
|
*/ |
570 |
|
static final class CitySet { |
571 |
< |
final int XMAX = 1000; |
572 |
< |
final int YMAX = 1000; |
571 |
> |
// Scale ints to doubles in [0,1) |
572 |
> |
static final double PSCALE = (double)0x80000000L; |
573 |
> |
|
574 |
|
final int length; |
575 |
< |
final int xPts[]; |
576 |
< |
final int yPts[]; |
577 |
< |
final double distances[]; |
578 |
< |
|
579 |
< |
CitySet(int n, LoopHelpers.SimpleRandom random) { |
575 |
> |
final int[] xPts; |
576 |
> |
final int[] yPts; |
577 |
> |
final float[][] distances; |
578 |
> |
|
579 |
> |
CitySet(int n) { |
580 |
|
this.length = n; |
581 |
< |
xPts = new int[n]; |
582 |
< |
yPts = new int [n]; |
581 |
> |
this.xPts = new int[n]; |
582 |
> |
this.yPts = new int[n]; |
583 |
> |
this.distances = new float[n][n]; |
584 |
> |
|
585 |
> |
RNG random = new RNG(); |
586 |
|
for (int i = 0; i < n; i++) { |
587 |
< |
xPts[i] = random.next() % XMAX; |
588 |
< |
yPts[i] = random.next() % YMAX; |
587 |
> |
xPts[i] = (random.next() & 0x7FFFFFFF); |
588 |
> |
yPts[i] = (random.next() & 0x7FFFFFFF); |
589 |
|
} |
590 |
< |
distances = new double[n * n]; |
590 |
> |
|
591 |
|
for (int i = 0; i < n; i++) { |
592 |
|
for (int j = 0; j < n; j++) { |
593 |
< |
double dX = (double)(xPts[i] - xPts[j]); |
594 |
< |
double dY = (double)(yPts[i] - yPts[j]); |
595 |
< |
distances[i + j * n] = Math.hypot(dX, dY); |
593 |
> |
double dX = (xPts[i] - xPts[j]) / PSCALE; |
594 |
> |
double dY = (yPts[i] - yPts[j]) / PSCALE; |
595 |
> |
distances[i][j] = (float)Math.hypot(dX, dY); |
596 |
|
} |
597 |
|
} |
598 |
|
} |
599 |
< |
|
599 |
> |
|
600 |
|
// Retrieve the cached distance between a pair of cities |
601 |
< |
double distanceBetween(int idx1, int idx2) { |
602 |
< |
return distances[idx1 + idx2 * length]; |
601 |
> |
float distanceBetween(int i, int j) { |
602 |
> |
return distances[i][j]; |
603 |
|
} |
604 |
|
} |
605 |
|
|
606 |
< |
static final class Runner implements Runnable { |
607 |
< |
final Population pop; |
608 |
< |
final CyclicBarrier b; |
609 |
< |
final int nGen; |
610 |
< |
final int tid; |
611 |
< |
static final boolean verbose = false; |
612 |
< |
|
613 |
< |
Runner(int tid, Population p, CyclicBarrier b, int n) { |
614 |
< |
this.tid = tid; |
615 |
< |
this.pop = p; |
616 |
< |
this.b = b; |
617 |
< |
this.nGen = n; |
606 |
> |
/** |
607 |
> |
* Cheap XorShift random number generator |
608 |
> |
*/ |
609 |
> |
static final class RNG { |
610 |
> |
/** Seed generator for XorShift RNGs */ |
611 |
> |
static final Random seedGenerator = new Random(); |
612 |
> |
|
613 |
> |
int seed; |
614 |
> |
RNG(int seed) { this.seed = seed; } |
615 |
> |
RNG() { this.seed = seedGenerator.nextInt(); } |
616 |
> |
|
617 |
> |
int next() { |
618 |
> |
int x = seed; |
619 |
> |
x ^= x << 6; |
620 |
> |
x ^= x >>> 21; |
621 |
> |
x ^= x << 7; |
622 |
> |
seed = x; |
623 |
> |
return x; |
624 |
|
} |
625 |
< |
|
625 |
> |
} |
626 |
> |
|
627 |
> |
static final class ProgressMonitor extends Thread { |
628 |
> |
final Population pop; |
629 |
> |
ProgressMonitor(Population p) { pop = p; } |
630 |
|
public void run() { |
631 |
+ |
double time = 0; |
632 |
|
try { |
633 |
< |
b.await(); |
634 |
< |
for (int i = 0; i < nGen; i++) { |
635 |
< |
if (verbose && 0 == tid && 0 == i % 1000) { |
636 |
< |
System.out.print("Gen " + i + " fitness:"); |
515 |
< |
System.out.print(" best=" + (int)pop.bestFitness()); |
516 |
< |
System.out.println("; avg=" + (int)pop.averageFitness()); |
517 |
< |
} |
518 |
< |
int matings = (((nGen - i) * 2) / (nGen)) + 1; |
519 |
< |
pop.nextGeneration(tid, matings); |
633 |
> |
while (!Thread.interrupted()) { |
634 |
> |
sleep(SNAPSHOT_RATE); |
635 |
> |
time += SNAPSHOT_RATE; |
636 |
> |
pop.printSnapshot(time / 1000.0); |
637 |
|
} |
638 |
< |
b.await(); |
522 |
< |
} |
523 |
< |
catch (InterruptedException e) { |
524 |
< |
e.printStackTrace(System.out); |
525 |
< |
System.exit(0); |
526 |
< |
} |
527 |
< |
catch (BrokenBarrierException e) { |
528 |
< |
e.printStackTrace(System.out); |
529 |
< |
System.exit(0); |
530 |
< |
} |
638 |
> |
} catch (InterruptedException ie) {} |
639 |
|
} |
640 |
|
} |
641 |
|
} |