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Comparing jsr166/src/test/loops/TSPExchangerTest.java (file contents):
Revision 1.1 by dl, Sun Aug 7 19:25:55 2005 UTC vs.
Revision 1.2 by dl, Sat Dec 10 20:08:51 2005 UTC

#	Line 1 | Line 1
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		}

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