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Comparing jsr166/src/test/loops/TSPExchangerTest.java (file contents):
Revision 1.2 by dl, Sat Dec 10 20:08:51 2005 UTC vs.
Revision 1.11 by jsr166, Sat Oct 16 16:22:57 2010 UTC

#	Line 11 | Line 11 | 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):
14	>	* genetic algorithm using an Exchanger.  A population of chromosomes is
15	>	* distributed among "subpops".  Each chromosomes represents a tour,
16	>	* and its fitness is the total tour length.
17		*
18	+	* A set of worker threads perform updates on subpops. The basic
19	+	* update step is:
20		* <ol>
21	<	*   <li> Select a breeder b from the pool
21	>	*   <li> Select a breeder b from the subpop
22		*   <li> Create a strand of its tour with a random starting point and length
23	<	*   <li> Offer the strand to the exchanger, receiving a strand from
24	<	*        another pool
25	<	*   <li> Combine b and the received strand using crossing function to
23	>	*   <li> Offer the strand to the exchanger, receiving a strand from
24	>	*        another subpop
25	>	*   <li> Combine b and the received strand using crossing function to
26		*        create new chromosome c.
27	<	*   <li> Replace a chromosome in the pool with c.
27	>	*   <li> Replace a chromosome in the subpop with c.
28		* </ol>
29		*
30	+	* This continues for a given number of generations per subpop.
31	+	* Because there are normally more subpops than threads, each worker
32	+	* thread performs small (randomly sized) run of updates for one
33	+	* subpop and then selects another. A run continues until there is at
34	+	* most one remaining thread performing updates.
35	+	*
36		* See below for more details.
32	–	* <p>
33	–	*
37		*/
38		public class TSPExchangerTest {
39	<	static final int DEFAULT_MAX_THREADS  =
40	<	(Runtime.getRuntime().availableProcessors() + 2);
39	>	static final int NCPUS = Runtime.getRuntime().availableProcessors();
40	>
41	>	/** Runs start with two threads, increasing by two through max */
42	>	static final int DEFAULT_MAX_THREADS  = Math.max(4, NCPUS + NCPUS/2);
43	>
44	>	/** The number of replication runs per thread value */
45	>	static final int DEFAULT_REPLICATIONS = 3;
46	>
47	>	/** If true, print statistics in SNAPSHOT_RATE intervals */
48	>	static boolean verbose = true;
49	>	static final long SNAPSHOT_RATE = 10000; // in milliseconds
50
51		/**
52		* The problem size. Each city is a random point. The goal is to
#	Line 42 | Line 54 | public class TSPExchangerTest {
54		*/
55		static final int DEFAULT_CITIES = 144;
56
57	<	// Tuning parameters.
57	>	// Tuning parameters.
58
59		/**
60	<	* The number of chromosomes per pool. Must be a power of two.
60	>	* The number of chromosomes per subpop. Must be a power of two.
61		*
62		* Smaller values lead to faster iterations but poorer quality
63		* results
64		*/
65	<	static final int DEFAULT_POOL_SIZE = 32;
65	>	static final int DEFAULT_SUBPOP_SIZE = 32;
66
67		/**
68	<	* The number of iterations per task. Convergence appears
68	>	* The number of iterations per subpop. Convergence appears
69		* to be roughly proportional to #cities-squared
70		*/
71		static final int DEFAULT_GENERATIONS = DEFAULT_CITIES * DEFAULT_CITIES;
72
73		/**
74	<	* The number of pools. The total population is #pools * poolSize,
74	>	* The number of subpops. The total population is #subpops * subpopSize,
75		* which should be roughly on the order of #cities-squared
76		*
77		* Smaller values lead to faster total runs but poorer quality
78		* results
79		*/
80	<	static final int DEFAULT_NPOOLS = DEFAULT_GENERATIONS / DEFAULT_POOL_SIZE;
80	>	static final int DEFAULT_NSUBPOPS = DEFAULT_GENERATIONS / DEFAULT_SUBPOP_SIZE;
81
82		/**
83		* The minimum length for a random chromosome strand.
#	Line 74 | Line 86 | public class TSPExchangerTest {
86		static final int MIN_STRAND_LENGTH = 3;
87
88		/**
89	<	* The probablility mask value for creating random strands,
89	>	* The probability mask value for creating random strands,
90		* that have lengths at least MIN_STRAND_LENGTH, and grow
91	<	* with exposnential decay 2^(-(1/(RANDOM_STRAND_MASK + 1)
91	>	* with exponential decay 2^(-(1/(RANDOM_STRAND_MASK + 1)
92		* Must be 1 less than a power of two.
93		*/
94		static final int RANDOM_STRAND_MASK = 7;
95
96		/**
97	<	* Probablility control for selecting breeders.
97	>	* Probability control for selecting breeders.
98		* Breeders are selected starting at the best-fitness chromosome,
99	<	* with exponentially decaying probablility
100	<	* 1 / (poolSize >>> BREEDER_DECAY).
99	>	* with exponentially decaying probability
100	>	* 1 / (subpopSize >>> BREEDER_DECAY).
101		*
102		* Larger values usually cause faster convergence but poorer
103		* quality results
#	Line 93 | Line 105 | public class TSPExchangerTest {
105		static final int BREEDER_DECAY = 1;
106
107		/**
108	<	* Probablility control for selecting dyers.
108	>	* Probability control for selecting dyers.
109		* Dyers are selected starting at the worst-fitness chromosome,
110	<	* with exponentially decaying probablility
111	<	* 1 / (poolSize >>> DYER_DECAY)
110	>	* with exponentially decaying probability
111	>	* 1 / (subpopSize >>> DYER_DECAY)
112		*
113		* Larger values usually cause faster convergence but poorer
114		* quality results
#	Line 104 | Line 116 | public class TSPExchangerTest {
116		static final int DYER_DECAY = 1;
117
118		/**
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	–	/**
119		* The set of cities. Created once per program run, to
120		* make it easier to compare solutions across different runs.
121		*/
122	<	static CitySet cities;
122	>	static CitySet cities;
123
124		public static void main(String[] args) throws Exception {
125		int maxThreads = DEFAULT_MAX_THREADS;
126		int nCities = DEFAULT_CITIES;
127	<	int poolSize = DEFAULT_POOL_SIZE;
127	>	int subpopSize = DEFAULT_SUBPOP_SIZE;
128		int nGen = nCities * nCities;
129	<	int nPools = nCities * nCities / poolSize;
129	>	int nSubpops = nCities * nCities / subpopSize;
130	>	int nReps = DEFAULT_REPLICATIONS;
131
132		try {
133		int argc = 0;
#	Line 137 | Line 136 | public class TSPExchangerTest {
136		if (option.equals("-c")) {
137		nCities = Integer.parseInt(args[argc]);
138		nGen = nCities * nCities;
139	<	nPools = nCities * nCities / poolSize;
139	>	nSubpops = nCities * nCities / subpopSize;
140		}
141		else if (option.equals("-p"))
142	<	poolSize = Integer.parseInt(args[argc]);
142	>	subpopSize = Integer.parseInt(args[argc]);
143		else if (option.equals("-g"))
144		nGen = Integer.parseInt(args[argc]);
145		else if (option.equals("-n"))
146	<	nPools = Integer.parseInt(args[argc]);
146	>	nSubpops = Integer.parseInt(args[argc]);
147	>	else if (option.equals("-q")) {
148	>	verbose = false;
149	>	argc--;
150	>	}
151	>	else if (option.equals("-r"))
152	>	nReps = Integer.parseInt(args[argc]);
153		else
154		maxThreads = Integer.parseInt(option);
155		argc++;
#	Line 157 | Line 162 | public class TSPExchangerTest {
162		System.out.print("TSPExchangerTest");
163		System.out.print(" -c " + nCities);
164		System.out.print(" -g " + nGen);
165	<	System.out.print(" -p " + poolSize);
166	<	System.out.print(" -n " + nPools);
165	>	System.out.print(" -p " + subpopSize);
166	>	System.out.print(" -n " + nSubpops);
167	>	System.out.print(" -r " + nReps);
168		System.out.print(" max threads " + maxThreads);
169		System.out.println();
170
171		cities = new CitySet(nCities);
172
173	<	for (int i = 2; i <= maxThreads; i += 2)
174	<	oneRun(i, nPools, poolSize, nGen);
173	>	if (false && NCPUS > 4) {
174	>	int h = NCPUS/2;
175	>	System.out.printf("Threads: %4d Warmup\n", h);
176	>	oneRun(h, nSubpops, subpopSize, nGen);
177	>	Thread.sleep(500);
178	>	}
179	>
180	>	int maxt = (maxThreads < nSubpops) ? maxThreads : nSubpops;
181	>	for (int j = 0; j < nReps; ++j) {
182	>	for (int i = 2; i <= maxt; i += 2) {
183	>	System.out.printf("Threads: %4d Replication: %2d\n", i, j);
184	>	oneRun(i, nSubpops, subpopSize, nGen);
185	>	Thread.sleep(500);
186	>	}
187	>	}
188		}
189
190		static void reportUsageErrorAndDie() {
191		System.out.print("usage: TSPExchangerTest");
192		System.out.print(" [-c #cities]");
193	<	System.out.print(" [-p #poolSize]");
193	>	System.out.print(" [-p #subpopSize]");
194		System.out.print(" [-g #generations]");
195	<	System.out.print(" [-n #pools]");
195	>	System.out.print(" [-n #subpops]");
196	>	System.out.print(" [-r #replications]");
197	>	System.out.print(" [-q <quiet>]");
198		System.out.print(" #threads]");
199		System.out.println();
200		System.exit(0);
201		}
202
203		/**
204	<	* Perform one run with the given parameters.  Each run completes
205	<	* when all but one of the tasks has finished.  The last remaining
206	<	* task may have no one left to exchange with, so the pool is
207	<	* abruptly terminated.
204	>	* Perform one run with the given parameters.  Each run complete
205	>	* when there are fewer than 2 active threads.  When there is
206	>	* only one remaining thread, it will have no one to exchange
207	>	* with, so it is terminated (via interrupt).
208		*/
209	<	static void oneRun(int nThreads, int nPools, int poolSize, int nGen)
209	>	static void oneRun(int nThreads, int nSubpops, int subpopSize, int nGen)
210		throws InterruptedException {
211	<	Population p = new Population(nThreads, nPools, poolSize, nGen);
211	>	Population p = new Population(nThreads, nSubpops, subpopSize, nGen);
212		ProgressMonitor mon = null;
213		if (verbose) {
214	+	p.printSnapshot(0);
215		mon = new ProgressMonitor(p);
216		mon.start();
217		}
196	–	p.printSnapshot(0);
218		long startTime = System.nanoTime();
219		p.start();
220	<	p.awaitTasks();
220	>	p.awaitDone();
221		long stopTime = System.nanoTime();
222		if (mon != null)
223		mon.interrupt();
224		p.shutdown();
225	<	Thread.sleep(100);
225	>	//        Thread.sleep(100);
226
227		long elapsed = stopTime - startTime;
228	<	long rate = elapsed / (nPools * nGen);
208	<	double secs = (double)elapsed / 1000000000.0;
228	>	double secs = (double) elapsed / 1000000000.0;
229		p.printSnapshot(secs);
210	–	System.out.printf("%10d ns per transfer\n", rate);
230		}
231
232
233		/**
234	<	* A Population creates the pools, tasks, and threads for a run
234	>	* A Population creates the subpops, subpops, and threads for a run
235		* and has control methods to start, stop, and report progress.
236		*/
237		static final class Population {
238	<	final Task[] tasks;
238	>	final Worker[] threads;
239	>	final Subpop[] subpops;
240		final Exchanger<Strand> exchanger;
221	–	final ThreadPoolExecutor exec;
241		final CountDownLatch done;
242		final int nGen;
243	<	final int poolSize;
243	>	final int subpopSize;
244		final int nThreads;
245
246	<	Population(int nThreads, int nPools, int poolSize, int nGen) {
246	>	Population(int nThreads, int nSubpops, int subpopSize, int nGen) {
247		this.nThreads = nThreads;
248		this.nGen = nGen;
249	<	this.poolSize = poolSize;
249	>	this.subpopSize = subpopSize;
250		this.exchanger = new Exchanger<Strand>();
251	<	this.done = new CountDownLatch(nPools-1);
252	<	this.tasks = new Task[nPools];
253	<	for (int i = 0; i < nPools; i++)
254	<	tasks[i] = new Task(this);
255	<	BlockingQueue<Runnable> tq =
256	<	new LinkedBlockingQueue<Runnable>();
257	<	this.exec = new ThreadPoolExecutor(nThreads, nThreads,
258	<	0L, TimeUnit.MILLISECONDS,
259	<	tq);
260	<	exec.prestartAllCoreThreads();
251	>	this.done = new CountDownLatch(nThreads - 1);
252	>
253	>	this.subpops = new Subpop[nSubpops];
254	>	for (int i = 0; i < nSubpops; i++)
255	>	subpops[i] = new Subpop(this);
256	>
257	>	this.threads = new Worker[nThreads];
258	>	int maxExchanges = nGen * nSubpops / nThreads;
259	>	for (int i = 0; i < nThreads; ++i) {
260	>	threads[i] = new Worker(this, maxExchanges);
261	>	}
262	>
263		}
264
244	–	/** Start the tasks */
265		void start() {
266	<	for (int i = 0; i < tasks.length; i++)
267	<	exec.execute(tasks[i]);
266	>	for (int i = 0; i < nThreads; ++i) {
267	>	threads[i].start();
268	>	}
269		}
270
271		/** Stop the tasks */
272		void shutdown() {
273	<	exec.shutdownNow();
273	>	for (int i = 0; i < threads.length; ++ i)
274	>	threads[i].interrupt();
275		}
276
277	<	/** Called by task upon terminations */
256	<	void taskDone() {
277	>	void threadDone() {
278		done.countDown();
279		}
280
281	<	/** Wait for (all but one) task to complete */
282	<	void awaitTasks() throws InterruptedException {
281	>	/** Wait for tasks to complete */
282	>	void awaitDone() throws InterruptedException {
283		done.await();
284		}
285
286	<	/**
287	<	* Called by a task to resubmit itself after completing
288	<	* fewer than nGen iterations.
289	<	*/
290	<	void resubmit(Task task) {
270	<	exec.execute(task);
286	>	int totalExchanges() {
287	>	int xs = 0;
288	>	for (int i = 0; i < threads.length; ++i)
289	>	xs += threads[i].exchanges;
290	>	return xs;
291		}
292
293	+	/**
294	+	* Prints statistics, including best and worst tour lengths
295	+	* for points scaled in [0,1), scaled by the square root of
296	+	* number of points. This simplifies checking results.  The
297	+	* expected optimal TSP for random points is believed to be
298	+	* around 0.76 * sqrt(N). For papers discussing this, see
299	+	* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html
300	+	*/
301		void printSnapshot(double secs) {
302	<	int gens = 0;
303	<	double best = Double.MAX_VALUE;
304	<	double worst = 0;
305	<	for (int k = 0; k < tasks.length; ++k) {
306	<	gens += tasks[k].gen;
307	<	Chromosome[] cs = tasks[k].chromosomes;
308	<	float b = cs[0].fitness;
309	<	if (b < best)
310	<	best = b;
311	<	float w = cs[cs.length-1].fitness;
312	<	if (w > worst)
313	<	worst = w;
314	<	}
315	<	int avegen = (done.getCount() == 0)? nGen : gens / tasks.length;
316	<	System.out.printf("Time:%9.3f Best:%9.3f Worst:%9.3f Gen:%6d Threads:%4d\n",
317	<	secs, best, worst, avegen, nThreads);
318	<	}
319	<
320	<	float averageFitness() { // currently unused
321	<	float total = 0;
322	<	int count = 0;
323	<	for (int k = 0; k < tasks.length; ++k) {
324	<	Chromosome[] cs = tasks[k].chromosomes;
325	<	for (int i = 0; i < cs.length; i++)
326	<	total += cs[i].fitness;
327	<	count += cs.length;
302	>	int xs = totalExchanges();
303	>	long rate = (xs == 0) ? 0L : (long) ((secs * 1000000000.0) / xs);
304	>	Chromosome bestc = subpops[0].chromosomes[0];
305	>	Chromosome worstc = bestc;
306	>	for (int k = 0; k < subpops.length; ++k) {
307	>	Chromosome[] cs = subpops[k].chromosomes;
308	>	if (cs[0].fitness < bestc.fitness)
309	>	bestc = cs[0];
310	>	int w = cs[cs.length-1].fitness;
311	>	if (cs[cs.length-1].fitness > worstc.fitness)
312	>	worstc = cs[cs.length-1];
313	>	}
314	>	double sqrtn = Math.sqrt(cities.length);
315	>	double best = bestc.unitTourLength() / sqrtn;
316	>	double worst = worstc.unitTourLength() / sqrtn;
317	>	System.out.printf("N:%4d T:%8.3f B:%6.3f W:%6.3f X:%9d R:%7d\n",
318	>	nThreads, secs, best, worst, xs, rate);
319	>	//            exchanger.printStats();
320	>	//            System.out.print(" s: " + exchanger.aveSpins());
321	>	//            System.out.print(" p: " + exchanger.aveParks());
322	>	}
323	>	}
324	>
325	>	/**
326	>	* Worker threads perform updates on subpops.
327	>	*/
328	>	static final class Worker extends Thread {
329	>	final Population pop;
330	>	final int maxExchanges;
331	>	int exchanges;
332	>	final RNG rng = new RNG();
333	>
334	>	Worker(Population pop, int maxExchanges) {
335	>	this.pop = pop;
336	>	this.maxExchanges = maxExchanges;
337	>	}
338	>
339	>	/**
340	>	* Repeatedly, find a subpop that is not being updated by
341	>	* another thread, and run a random number of updates on it.
342	>	*/
343	>	public void run() {
344	>	try {
345	>	int len = pop.subpops.length;
346	>	int pos = (rng.next() & 0x7FFFFFFF) % len;
347	>	while (exchanges < maxExchanges) {
348	>	Subpop s = pop.subpops[pos];
349	>	AtomicBoolean busy = s.busy;
350	>	if (!busy.get() && busy.compareAndSet(false, true)) {
351	>	exchanges += s.runUpdates();
352	>	busy.set(false);
353	>	pos = (rng.next() & 0x7FFFFFFF) % len;
354	>	}
355	>	else if (++pos >= len)
356	>	pos = 0;
357	>	}
358	>	pop.threadDone();
359	>	} catch (InterruptedException fallthrough) {
360		}
301	–	return total/(float)count;
361		}
362		}
363
364		/**
365	<	* A Task updates its pool of chromosomes..
365	>	* A Subpop maintains a set of chromosomes..
366		*/
367	<	static final class Task implements Runnable {
368	<	/** The pool of chromosomes, kept in sorted order */
367	>	static final class Subpop {
368	>	/** The chromosomes, kept in sorted order */
369		final Chromosome[] chromosomes;
370	+	/** The parent population */
371		final Population pop;
372	<	/** The common exchanger, same for all tasks */
372	>	/** Reservation bit for worker threads */
373	>	final AtomicBoolean busy;
374	>	/** The common exchanger, same for all subpops */
375		final Exchanger<Strand> exchanger;
376		/** The current strand being exchanged */
377		Strand strand;
378		/** Bitset used in cross */
379		final int[] inTour;
380		final RNG rng;
381	<	final int poolSize;
320	<	final int nGen;
321	<	int gen;
381	>	final int subpopSize;
382
383	<	Task(Population pop) {
383	>	Subpop(Population pop) {
384		this.pop = pop;
385	<	this.nGen = pop.nGen;
326	<	this.gen = 0;
327	<	this.poolSize = pop.poolSize;
385	>	this.subpopSize = pop.subpopSize;
386		this.exchanger = pop.exchanger;
387	+	this.busy = new AtomicBoolean(false);
388		this.rng = new RNG();
389		int length = cities.length;
390		this.strand = new Strand(length);
391		this.inTour = new int[(length >>> 5) + 1];
392	<	this.chromosomes = new Chromosome[poolSize];
393	<	for (int j = 0; j < poolSize; ++j)
392	>	this.chromosomes = new Chromosome[subpopSize];
393	>	for (int j = 0; j < subpopSize; ++j)
394		chromosomes[j] = new Chromosome(length, rng);
395		Arrays.sort(chromosomes);
396		}
397
398		/**
399	<	* Run one or more update cycles.
399	>	* Run a random number of updates.  The number of updates is
400	>	* at least 1 and no more than subpopSize.  This
401	>	* controls the granularity of multiplexing subpop updates on
402	>	* to threads. It is small enough to balance out updates
403	>	* across tasks, but large enough to avoid having runs
404	>	* dominated by subpop selection. It is randomized to avoid
405	>	* long runs where pairs of subpops exchange only with each
406	>	* other.  It is hardwired because small variations of it
407	>	* don't matter much.
408	>	*
409	>	* @param g the first generation to run.
410		*/
411	<	public void run() {
412	<	try {
413	<	for (;;) {
414	<	update();
415	<	if (++gen >= nGen) {
347	<	pop.taskDone();
348	<	return;
349	<	}
350	<	if ((rng.next() & RESUBMIT_MASK) == 1) {
351	<	pop.resubmit(this);
352	<	return;
353	<	}
354	<	}
355	<	} catch (InterruptedException ie) {
356	<	pop.taskDone();
357	<	}
411	>	int runUpdates() throws InterruptedException {
412	>	int n = 1 + (rng.next() & ((subpopSize << 1) - 1));
413	>	for (int i = 0; i < n; ++i)
414	>	update();
415	>	return n;
416		}
417
418		/**
419	<	* Choose a breeder, exchange strand with another pool, and
419	>	* Choose a breeder, exchange strand with another subpop, and
420		* cross them to create new chromosome to replace a chosen
421		* dyer.
422		*/
#	Line 379 | Line 437 | public class TSPExchangerTest {
437		* @return index of selected breeder
438		*/
439		int chooseBreeder() {
440	<	int mask = (poolSize >>> BREEDER_DECAY) - 1;
440	>	int mask = (subpopSize >>> BREEDER_DECAY) - 1;
441		int b = 0;
442		while ((rng.next() & mask) != mask) {
443	<	if (++b >= poolSize)
443	>	if (++b >= subpopSize)
444		b = 0;
445		}
446		return b;
#	Line 390 | Line 448 | public class TSPExchangerTest {
448
449		/**
450		* Choose a chromosome that will be replaced, with
451	<	* exponentially decreasing probablility starting at
451	>	* exponentially decreasing probability starting at
452		* worst, ignoring the excluded index
453		* @param exclude index to ignore; use -1 to not exclude any
454		* @return index of selected dyer
455		*/
456		int chooseDyer(int exclude) {
457	<	int mask = (poolSize >>> DYER_DECAY)  - 1;
458	<	int d = poolSize - 1;
457	>	int mask = (subpopSize >>> DYER_DECAY)  - 1;
458	>	int d = subpopSize - 1;
459		while (d == exclude || (rng.next() & mask) != mask) {
460		if (--d < 0)
461	<	d = poolSize - 1;
461	>	d = subpopSize - 1;
462		}
463		return d;
464		}
465
466	<	/**
466	>	/**
467		* Select a random strand of b's.
468		* @param breeder the breeder
469		*/
#	Line 450 | Line 508 | public class TSPExchangerTest {
508		int first = cs[0];
509		int j = 0;
510		int[] bs = breeder.alleles;
511	<	while (bs[j] != first)
511	>	while (bs[j] != first)
512		++j;
513
514		// Append remaining b's that aren't already in tour
#	Line 459 | Line 517 | public class TSPExchangerTest {
517		int x = bs[j];
518		if ((inTour[x >>> 5] & (1 << (x & 31))) == 0)
519		cs[i++] = x;
520	<	}
520	>	}
521
522		}
523
524		/**
525		* Fix the sort order of a changed Chromosome c at position k
526		* @param c the chromosome
527	<	* @param k the index
527	>	* @param k the index
528		*/
529		void fixOrder(Chromosome c, int k) {
530		Chromosome[] cs = chromosomes;
531	<	float oldFitness = c.fitness;
531	>	int oldFitness = c.fitness;
532		c.recalcFitness();
533	<	float newFitness = c.fitness;
533	>	int newFitness = c.fitness;
534		if (newFitness < oldFitness) {
535		int j = k;
536		int p = j - 1;
#	Line 500 | Line 558 | public class TSPExchangerTest {
558		/** Index of cities in tour order */
559		final int[] alleles;
560		/** Total tour length */
561	<	float fitness;
561	>	int fitness;
562
563	<	/**
563	>	/**
564		* Initialize to random tour
565		*/
566		Chromosome(int length, RNG random) {
#	Line 519 | Line 577 | public class TSPExchangerTest {
577		}
578
579		public int compareTo(Object x) { // to enable sorting
580	<	float xf = ((Chromosome)x).fitness;
581	<	float f = fitness;
582	<	return ((f == xf)? 0 :((f < xf)? -1 : 1));
580	>	int xf = ((Chromosome) x).fitness;
581	>	int f = fitness;
582	>	return ((f == xf) ? 0 :((f < xf) ? -1 : 1));
583		}
584
585		void recalcFitness() {
586		int[] a = alleles;
587		int len = a.length;
588		int p = a[0];
589	<	float f = cities.distanceBetween(a[len-1], p);
589	>	long f = cities.distanceBetween(a[len-1], p);
590		for (int i = 1; i < len; i++) {
591		int n = a[i];
592		f += cities.distanceBetween(p, n);
593		p = n;
594		}
595	<	fitness = f;
595	>	fitness = (int) (f / len);
596	>	}
597	>
598	>	/**
599	>	* Return tour length for points scaled in [0, 1).
600	>	*/
601	>	double unitTourLength() {
602	>	int[] a = alleles;
603	>	int len = a.length;
604	>	int p = a[0];
605	>	double f = cities.unitDistanceBetween(a[len-1], p);
606	>	for (int i = 1; i < len; i++) {
607	>	int n = a[i];
608	>	f += cities.unitDistanceBetween(p, n);
609	>	p = n;
610	>	}
611	>	return f;
612		}
613
614	<	void validate() { // Ensure that this is a valid tour.
614	>	/**
615	>	* Check that this tour visits each city
616	>	*/
617	>	void validate() {
618		int len = alleles.length;
619		boolean[] used = new boolean[len];
620	<	for (int i = 0; i < len; ++i)
620	>	for (int i = 0; i < len; ++i)
621		used[alleles[i]] = true;
622	<	for (int i = 0; i < len; ++i)
622	>	for (int i = 0; i < len; ++i)
623		if (!used[i])
624		throw new Error("Bad tour");
625		}
#	Line 550 | Line 627 | public class TSPExchangerTest {
627		}
628
629		/**
630	<	* A Strand is a random sub-sequence of a Chromosome.  Each task
630	>	* A Strand is a random sub-sequence of a Chromosome.  Each subpop
631		* creates only one strand, and then trades it with others,
632		* refilling it on each iteration.
633		*/
#	Line 561 | Line 638 | public class TSPExchangerTest {
638		}
639
640		/**
641	<	* 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
641	>	* A collection of (x,y) points that represent cities.
642		*/
643		static final class CitySet {
571	–	// Scale ints to doubles in [0,1)
572	–	static final double PSCALE = (double)0x80000000L;
644
645		final int length;
646		final int[] xPts;
647		final int[] yPts;
648	<	final float[][] distances;
648	>	final int[][] distances;
649
650		CitySet(int n) {
651		this.length = n;
652		this.xPts = new int[n];
653		this.yPts = new int[n];
654	<	this.distances = new float[n][n];
654	>	this.distances = new int[n][n];
655
656		RNG random = new RNG();
657		for (int i = 0; i < n; i++) {
#	Line 590 | Line 661 | public class TSPExchangerTest {
661
662		for (int i = 0; i < n; i++) {
663		for (int j = 0; j < n; j++) {
664	<	double dX = (xPts[i] - xPts[j]) / PSCALE;
665	<	double dY = (yPts[i] - yPts[j]) / PSCALE;
666	<	distances[i][j] = (float)Math.hypot(dX, dY);
664	>	double dx = (double) xPts[i] - (double) xPts[j];
665	>	double dy = (double) yPts[i] - (double) yPts[j];
666	>	double dd = Math.hypot(dx, dy) / 2.0;
667	>	long ld = Math.round(dd);
668	>	distances[i][j] = (ld >= Integer.MAX_VALUE) ?
669	>	Integer.MAX_VALUE : (int) ld;
670		}
671		}
672		}
673
674	<	// Retrieve the cached distance between a pair of cities
675	<	float distanceBetween(int i, int j) {
674	>	/**
675	>	* Returns the cached distance between a pair of cities.
676	>	*/
677	>	int distanceBetween(int i, int j) {
678		return distances[i][j];
679		}
680	+
681	+	// Scale ints to doubles in [0,1)
682	+	static final double PSCALE = (double) 0x80000000L;
683	+
684	+	/**
685	+	* Returns distance for points scaled in [0,1). This simplifies
686	+	* checking results.  The expected optimal TSP for random
687	+	* points is believed to be around 0.76 * sqrt(N). For papers
688	+	* discussing this, see
689	+	* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html
690	+	*/
691	+	double unitDistanceBetween(int i, int j) {
692	+	double dx = ((double) xPts[i] - (double) xPts[j]) / PSCALE;
693	+	double dy = ((double) yPts[i] - (double) yPts[j]) / PSCALE;
694	+	return Math.hypot(dx, dy);
695	+	}
696	+
697		}
698
699		/**
#	Line 612 | Line 705 | public class TSPExchangerTest {
705
706		int seed;
707		RNG(int seed) { this.seed = seed; }
708	<	RNG()         { this.seed = seedGenerator.nextInt(); }
708	>	RNG()         { this.seed = seedGenerator.nextInt() | 1;  }
709
710		int next() {
711		int x = seed;

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