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README.md

Traveling Salesman Problem

Coursework #2

CS454 AI Based Software Engineering

Oct 10th 2019

20170504 Juan Lee

[TOC]

1. Introduction

This assignment is to make a solver for Traveling Salesman Problem (TSP), which is known as NP problem so that we cannot solve TSP in polynomial time (under P ≠ NP). So, the purpose of this assignment is to lower the result as many as possible using stochastic algorithms and heuristics.

I have used four different algorithms, Genetic Algorithm (GA), Greedy Algorithm, Ant Colony Optimization (ACO), and modified Minimum Spanning Tree (MST) algorithm. Note that MST algorithm is deterministic, and I used this algorithm only for generating initial data for other algorithms or for examining the escape from local optima.

1.1 Environment

Since this assignment is highly affected by computing power, so I note my testing environment. All the codes are written in Python 3.7, and tested on macOS Mojave 10.14.6 with processor 2.2GHz Intel Core i7 and memory 16GB 2400MHz DDR4.

Also, I used a280.tsp for examination because it contains proper (not so many not so little) number of nodes to compute in my computer. However, some nodes in a280.tsp have exactly same value of x and y, so it does not satisfy triangular inequality. I added some error handling for this case. (For example, immediately move to other node if they are in same location)

1.2 Code Structure

This is the code structure, and all the python programs are in tsp folder. a280.tsp, att532.tsp and rl11849.tsp are test files. tsp.pyis executable file for all the algorithms. I will explain it in 1.3. aco.py is for Ant Colony Optimization algorithm. See function aco if you want to look into my algorithm. ga.py is for Genetic Algorithm, starting from function ga. greedy.py is greedy algorithm, starting from function greedy. mst.py is modified version of Minimum Spanning Tree algorithm, starting from function mst.

README.md is markdown version of this report, and report.pdf is this file.

tsp
├── tsp
|   ├── a280.tsp
|   ├── att532.tsp
|   ├── rl11849.tsp
|   ├── tsp.py
|   ├── aco.py
|   ├── ga.py
|   ├── greedy.py
|   └── mst.py
├── README.md
└── report.pdf

Note that all the algorithms can be run separately, but I recommend you to follow 1.3.

1.3 How to Run TSP Solver

This is how you start my TSP Solver with all default values,

$ cd ./tsp
$ python3 tsp.py rl11849.tsp

I provide many useful flags to control algorithm, constants, strategy, or system functions.

1.3.1 General Flags

-a alg: choose algorithm. alg is one of aco, ga, greedy, or spanning. greedy is default.

-h: show help

-l: print log. False is default.

1.3.2 Ant Colony Optimization Flags

For detailed information, read related chapter.

-p size: the number of ants. 10 is default.

-w weight: pheromone weight. 0.5 is default. length weight is set (1-weight)

-f size: the number of generations. 100 is default.

-i value: initial pheromone. 1 is default

-e length: estimated shortest tour for ACO. 1000000 is default

-r rate: evaporation rate. 0.1 is default

-x strategy: examination strategy. one of tester and mst. mst is default.

1.3.3 Genetic Algorithm Flags

For detailed informaion, read related chapter.

-p size: the size of population. 150 is default

-w rate: selection rate. 0.1 is default.

-f size: the number of generations. 100 is default.

-x value: crossover strategy. value is one of my, cx, pmx, and no. cx crossover is default.

-s value: selection strategy. value is one of overselect and elitism. overselect is default.

-m rate: mutation rate. 0.05 is default.

-gl length: gene max length for my crossover algorithm. 100 is default.

-ie rate: ratio of maintaining best solution at initializing

-im rate: ratio of generating mutated second-best solution at initializing.

1.3.4 Greedy Algorithm Flags

For detailed information, read related chapter.

-i node: initial node. randomly chosen node is default.

1.3.5 Example Usages

This is an example of tsp solver solving rl11849.tsp using Ant Colony Optimization algorithm with one ant, almost (0.9) depending on length, and run 1000 generations.

$ python3 tsp.py rl11849.tsp -a aco -p 1 -f 0.1 -g 1000

This is an example of tsp solver solving rl11849.tsp using Greedy Algorithm starting from node 52. (now deterministic)

$ python3 tsp.py rl11849.tsp -a greedy -i 52

2. Genetic Algorithm

Genetic Algorithm is a bio-inspired algorithm from the theory of evolution. From randomly generated origin population, their children inherits their parents' genes. The genes are mutated, crossover-ed, and selected from their parents. For TSP, a permutation of n nodes represents a gene. You can see the code in ga.py.

2.1 Initialization

# initialize
try:
	population = initializeFromExisting("solution.csv", nodes, POPULATION_SIZE)
except:
	population = initializeWithRandom(nodes, POPULATION_SIZE)

For genetic algorithm, since it takes too much time to compute from very initial, I made that initializing can begin from an existing file. If there exists solution.csv, it creates population using the solution, or, creates population randomly with size of POPULATIN_SIZE.

initializingFromExisting uses two constants, INITIALIZE_SAME_AS_EXISTING_RATE and INITIALIZE_MUTATE_ONCE_FROM_EXISTING_RATE, which are given by flags -ie and -im respectively. -ie is the ratio of genes which are same as the solution and -im is the ratio of genes which is made by one mutation from the solution.

2.2 Generations

# generations
m = 99999999999999
for i in range(GENERATION):
	population, mDist, mPath = select(nodes, population)

	if m > mDist:
		m = mDist
		saveToFile("solution.csv", mPath)

print(mDist)

For each generation, it simply selects from population, and assigns the results to population back. All the other processes like mutation or crossover happen in the select function. Also, at the end of each operation, if the current population makes shorter distance than saved current shortest value, it stores the sequence of nodes to solution.csv file.

The number of generations is in GENERATION variable which can be controlled by -f flag.

2.3 Selection

For the selection algorithm, I implemented two strategies, elitism and overselect. Elitism is a way to choose only elites from the population, and all the non-elites genes are discarded. This strategy is good for solving TSP faster, however, it easy falls down to local optima and difficult to escape it. The only way to escape the local optima is mutation in this case.

Overselect is a way to use both elites and non-elites. It generates $(1-r)\times100%$ of new poplutions using top $r \times100%$ of parents. Also, by using bottom $(1-r) \times 100%$ of parents, generates $r\times100%$ of new population. So, if we set $r<0.5$, it means that we assume some of top parents are more productive than other bottom parents, and this makes sense in aspect of the theory of evolution. For my algorithm, I use overselect strategy as a default selection algorithm.

All the other operations like mutation and crossover happen in the selection, thus both selection strategies have same structure like below:

rp1 = random.choice(bottom)
rp2 = random.choice(bottom)
np1, np2 = crossOver(rp1, rp2)
np1 = mutate(np1, GENE_MUTATE_RATE)
np2 = mutate(np2, GENE_MUTATE_RATE)
newpopulation += [np1, np2]

You can control the selection strategy by giving -s flag. If not given, overselect is default strategy.

2.4 CrossOver

For the crossover algorithm, I implemeneted three strategies, cx, pmx2, and my own strategy. The cycle crossover (CX) strategy is literally choose crossing genes cyclic, staring from the fixed first gene.

# cxCrossOver: path, path -> two paths
# - cross over using cycle crossover algorithm
def cxCrossOver(x1, x2):
    y1 = [-1] * len(x1)
    y2 = [-1] * len(x2)

    y1[0] = x1[0]
    y2[0] = x2[0]
    i = 0

    # do once first
    while x2[i] not in y1:
        j = x1.index(x2[i])
        y1[j] = x1[j]
        y2[j] = x2[j]
        i = j

    for i in range(len(y1)):
        if y1[i] == -1:
            y1[i] = x2[i]
            y2[i] = x1[i]

    return (y1, y2)

The partially-mapped crossover 2 (PMX2) strategy is a way to map some part of child with proper part of parent. The two crossover points are randomly chosen.

# pmxCrossOver
def pmxCrossOver(p1,p2):
    y1 = [-1] * len(p1)
    y2 = [-1] * len(p2)

    a = random.randint(1, len(p1) - 1)
    b = random.randint(a, len(p1))

    for i in range(a, b):
        y1[i] = p2[i]
        y2[i] = p1[i]

    for i in (list(range(a)) + list(range(b, len(p1)))):
        if p1[i] in y1:
            t = p2[i]
            while (t not in p1[a:b]) or (t in y1):
                t = p2[p1.index(t)]
            y1[i] = t
        else:
            y1[i] = p1[i]

    for i in (list(range(a)) + list(range(b, len(p1)))):
        if p2[i] in y2:
            t = p1[i]
            while (t not in p2[a:b]) or (t in y2):
                t = p1[p2.index(t)]
            y2[i] = t
        else:
            y2[i] = p2[i]

    return y1, y2

I made my own, which chooses some part with randomly chosen starting point and randomly chosen length, and generate a child by mapping the part to another parent's genes containing same element as chosen one of the first parent with maintaining the order.

def myCrossOver(p1, p2):
    s = random.randint(0, len(p1)-1)
    e = random.randint(s, s + GENE_MAX_LENGTH)
    e = min(e, len(p1) - 1)
    np1 = p1[:]
    np2 = p2[:]
    exchangePart = p1[s:e+1]

    idx1 = s
    idx2 = 0
    while idx1 < e + 1:
        if np2[idx2] in exchangePart:
            np1[idx1], np2[idx2] = np2[idx2], np1[idx1]
            idx1 += 1
        idx2 += 1
    return (np1, np2)

You can select crossover algorithm by giving -x flag. If not given, cx is default strategy.

2.5 Mutation

Mutation happens very simply. It randomly chooses two nodes from the path and exchange them with the probability of $r$. You can give mutation rate by giving -m flag.

# mutate: path, r -> path
# - mutate if p < r
def mutate(path, r):
    if random.random() < r:
        id1 = random.choice(path)
        id2 = random.choice(path)
        while id2 == id1:
            id2 = random.choice(path)

        idx1 = path.index(id1)
        idx2 = path.index(id2)
        path[idx1], path[idx2] = path[idx2], path[idx1]
    return path

2.6 Results

I compare and contrasts the strategies by using a280.tsp test file.

2.6.1 Different Population Size

Without changing any other strategies, I firstly change the population size and examine the performance.

Trial Size = 10 Size = 100 Size = 1000
1 30460.455649649724 25352.835163090258 21041.18658386337
2 30729.02696903852 24939.693109770862 19314.39413706647
3 31683.85967369266 25002.99973184998 19986.05824402357
4 29757.224718621335 24733.104581769403 19453.74995919088
5 30482.94349892524 25341.2388277846 19189.361699694135
6 30781.89791055486 24803.997093830018 20149.337492665614
7 30018.89614246253 24574.891680816807 21160.422942459885
8 30672.166803238688 24847.465597062444 21514.984989394805
9 31109.233633432763 25525.746371621968 19000.181694585208
10 30872.232802828064 24175.81454369313 19808.01556129264
Mean 30656.7938 24929.7787 20061.7693

The results show that the larger the size is the shorter the distance is. This shows that the larger population has higher probabilty of making a good new population, and this is quite obvious result for genetic algorithm since it randomly generates initial population. Better gene can be generated randomly if we generate many.

2.6.2 Selection Strategies

With the population size 10 and size 1000, I compare and contrast two selection strategies, overselect and elitism. Since elitism is good for making local optima in short time and overselect is good for escaping the local optima, I choose two different population sizes which have big gap between them.

Trial Elitism 10 Elitism 1000 Overselect 10 Overselect 1000
1 30510.194875667552 21616.246254442594 30460.455649649724 21041.18658386337
2 29279.592644474524 20484.561476339168 30729.02696903852 19314.39413706647
3 31183.97859062004 19428.42690080714 31683.85967369266 19986.05824402357
4 29796.39836439586 21286.108913483822 29757.224718621335 19453.74995919088
5 31875.82138852167 20364.306127363405 30482.94349892524 19189.361699694135
6 30882.78732962493 20548.12156375085 30781.89791055486 20149.337492665614
7 31683.44879050855 20740.65354475836 30018.89614246253 21160.422942459885
8 31764.152436486384 20028.290764121946 30672.166803238688 21514.984989394805
9 31405.15422500531 21174.842252258568 31109.233633432763 19000.181694585208
10 28465.795302109942 21449.55394124934 30872.232802828064 19808.01556129264
Mean 30684.7324 20712.1112 30656.7938 20061.7693

I think the table shows interesting results. For the smaller population, the result is not that different. Even for some trials, elitism overwhelms overselection. However, for the larger population, it shows a little difference. We might be able to say, overselection works for larger group.

In order to see the affect of time, I tried same experiment with not population size but the number of generation. 10 and 1000 are big different for the number of generation, so I expected I can see the meaningful difference.

Trial Elitism 10 Elitism 1000 Overselect 10 Overselect 1000
1 30328.240003664523 14407.166153671325 30041.747013673696 14192.781275560304
2 30459.448668885045 14455.094888528356 30018.90631786845 14194.222175471314
3 29439.9270078868 14823.223997142872 29615.73423330859 14083.916770573058
4 30251.078506429414 15400.970945566358 30201.46528504951 14715.058855974881
5 29667.940096647286 14815.471576848886 30018.396171460336 14288.064754602869
6 30125.741034435126 14643.42720543723 29310.688018869536 14722.435966223005
7 29770.876315733636 14755.8422926035 29875.454444900017 14147.219644775054
8 31034.706129616465 14455.626629775428 30360.271005135113 14135.787863837482
9 29584.548985518322 15042.644712940515 30065.683227899404 13978.194673939834
10 30104.780853246684 14740.660006943404 31205.186684889366 14050.51880264258
Mean 30076.7288 14754.0128 30071.3532 14250.8201

Interestingly, or sadly, even the number of generation does not affect much to the selection algorithm. In conclusion, both generation and population size affect the selection algorithm little bit, and overselection makes shorter distance for both cases, but the difference was not big in both cases.

2.6.3 CrossOver Strategies

Without changing other variables, I examined three crossover strategies.

Trial CX PMX2 My Own
1 25352.835163090258 22571.49017244644 19269.256850288326
2 24939.693109770862 23382.24185649681 19027.871813272563
3 25002.99973184998 22005.260003693234 19658.50335392552
4 24733.104581769403 22365.456254633915 19667.68415827356
5 25341.2388277846 22401.252021651188 19892.36807519136
6 24803.997093830018 22755.408498791294 20052.63867263101
7 24574.891680816807 22877.140922180253 20490.284175843648
8 24847.465597062444 20942.105003045315 21071.46424847718
9 25525.746371621968 22112.87735018012 19905.79941010982
10 24175.81454369313 23116.77676596921 20664.955929152875
Mean 24929.7787 22453.0009 19970.0827

CrossOver experiment shows very and very interesting results. I don't know why... but my own strategy makes the best performance among cx, pmx2, and my own algorithm. I guess this is because of the feature of the dataset. So, I tried it once again with different dataset att532.tsp.

Trial CX PMX2 My Own
1 1330953.1915611054 1101964.5545933624 1149607.5829424844
2 1293981.4940828322 1166515.1067867687 1149568.20216079
3 1298075.0345198216 1180516.5668649592 1147990.7919919111
4 1302327.7208721414 1160887.0985638432 1125353.1535104103
5 1338449.4152270027 1209918.1285760172 1080512.3612170555
6 1313548.105376266 1152454.7271207392 1140845.7265408672
7 1283112.83597773 1250368.46920412 1101276.007987794
8 1307437.663505304 1160213.9918794332 1128387.7251622607
9 1320188.3696314606 1228162.9111157968 1160686.499887125
10 1315181.3331217095 1162941.3675968556 1112500.2120000038
Mean 1310325.52 1177394.29 1129672.83

Even for this dataset my algorithm makes better performance. This might be because of the small number of generation, so that my algorithm might accelerate selection for the early stages of evolution.

2.6.4 Mutation Rates

Without chainging other variables, I examined the effect of mutation rates. To show the affect, I selected 0.05, 0.5, and 1.

Trial 0.05 0.5 1
1 24771.860059512434 20036.09606684269 17891.016006750808
2 25281.151174722923 20830.187076556307 19174.3713364187
3 24807.884487502546 19912.1680467268 19413.816808463387
4 25551.723397809867 19487.87632149736 18917.30338384056
5 25308.399575240757 19396.925502599086 18771.399731605634
6 25511.663049045615 19846.984863860227 19085.076151436613
7 24745.020596720045 19956.225363605 18466.93846128218
8 25440.98455994306 20312.17824520457 18823.40850489367
9 24575.63612895698 20463.881049606374 19456.371370942266
10 25831.659765189946 20454.065790985558 18405.74611422217
Mean 25182.5983 20069.6588 18840.5448

The result for the mutation rates give another interesting insights. It shows a linear relationship between mutation rate and the distance, the bigger the mutation rate is, the shorter the result is. I guess this is because the small number of generation size. For the early steps of generations, mutation might work as one part of selection or crossover algorithm so that it accelerates the evolution.

3. Ant Colony Optimization

Ant Colony Optimization is a bio-inspired algorithm from ants. Since ants need to optimize their movements in their colony, they use pheromone. Instead of calculating the shortest distance or memorizing all the paths, they leave pheromone where they already passed. The leaving pheromone is determined by the distance of routes ants traveled with satisfying TSP condition, so that pheromone reinforces the path which leads the path shorter and shorter.

3.1 initializing

For the initialzing, I calculated all the possible distance of edges in Anthill, and by begin operation, we put ants at the random node in the anthill.

anthill = Anthill(nodes)
ants = [Ant(anthill, i) for i in range(ANT_NUMBER)]

for ant in ants:
	ant.begin()

3.2 generations

For each generation, ants travel the nodes under TSP conditions, which is All nodes must be visited exactly once. Because of this condition all the ants travel same length of route, so that we can find the end of travelling by checking one ant. At the end of each generation, we evaporate some pheromone. This helps to escape local optima.

for gen in range(GENERATION):
	while not ants[0].end():
		for ant in ants:
			ant.turn()
		anthill.evaporate()

		# reset
		for ant in ants:
			ant.begin()

3.3 ant moves

Basically, ants move to near nodes with the probability of:

$$ p^{k}{ij}={\tau^{\alpha}{ij}\eta^{\beta}{ij} \over \sum^{H}{h\in J^k}\tau^{\alpha}{ih}\eta^{\beta}{ih}} $$

, where $\eta_{ij} = {1 \over l_{ij}}$ is the visibility of the node $j$ from node $i$. Weights $\alpha$ and $\beta$ indicate the weights of length of pheromone. $J^k$ is a set of not visited nodes.

However, for my algorithm due to the different scale of length and pheromone, I re-scaled the distance into $[0, 1]$ range. I thought this is okay since I set a default value of the initial pheromone as 1 so the they are balanced. This calculation code is implemented in the code like below:

dist = anthill.edges[(i-1, current-1)]
eta = (anthill.weightMax - anthill.weightMin) / (dist - anthill.weightMin + 1)
tau = anthill.pheromone[(i-1, current-1)]

prob = (eta**WEIGHT_LENGTH) * (tau**WEIGHT_PHEROMONE)

3.4 Pheromone

The amount of pheromone ant leaves is calculated as

$$ \triangle \tau^{k}_{ij}={Q \over L^k} $$

, where $Q$ is the estimated length of shortest tour, and $L^k$ is the length of tour of ant $k$. This pheromone is evaporated at the end of each generation with the equation of $\tau^{t+1}{ij}=(1-\rho)\tau^{t}{ij}+\triangle T_{ij}$.

3.5 Make a Result

ACO Algorithm only tells us how to update the wieght of edges using pheromones. So, we need to make another heuristic to make a result.

I made another ant named tester, which examines the path by following the edges just like other ants.

3.5.1 Tester Ant

The first method is to use a tester ant. Just like other ants, one ant travels at the end of all generations, and calculate the route.

However, I thought this method is not good to calculate the result of the ACO algorithm because this does not fully use the results of pheromone, and still depends on probablity.

3.5.2 MST Algorithm

So I applied MST algorithm here (This is modified version, see chapter 5 of this report). Instead of edges, I used resulting pheromone to construct the minimum spanning tree.

3.6 Results

3.6.1 Tester Ant vs. MST Algorithm

In order to compare two resulting strategies, I tried a simple examination here. Because of the low running speed of the program, I used three ants and only ten generations.

Trial Tester Ant MST
1 29344.900515504803 5011.152731234156
2 31570.233159653544 5226.745089956175
3 32272.264801336354 5060.589234206545
4 31616.03777039962 5169.505968885088
5 30009.35924847931 5144.259770795619
6 29557.284122006185 5242.220592610473
7 30392.243853257718 5301.77732819374
8 30271.63617285205 5256.381032045911
9 30364.589177747304 5311.122735747752
10 31521.634659424817 5076.068229533991
Mean 30692.0183 5179.98227

This is somewhat obvious result, but I thought we do not have to depend on the probability at the end of algorithm, since there is no futher step of optimization.

3.6.2 Pheromone Depending Weights

I tried several experiment by controlling pheromone depending weights. 1 means ants entirely depend on pheromone, 0 means ants entirely depend on length. Note that 0 does not mean it is greedy algorithm because ants always choose the way based on the probability. For this test, I choose three values, 1, 0.5, and 0.

Trial weight = 1 weight = 0.5 weight = 0
1 4843.7716611159385 5212.486673452476 7172.272063841112
2 4885.837124825724 5013.211876883836 7537.035290744606
3 4866.320821127782 5160.095072186534 7553.433035646136
4 4905.381306528206 5285.732278773989 7474.341857244992
5 4881.656896849474 5188.948932083258 7154.505787102157
6 4879.10952817189 5073.5432764749075 7342.27138801812
7 4841.279422375462 5179.611731956752 7323.690940886781
8 4868.411647416126 5180.886085300517 7707.111286945524
9 4829.814225990952 5048.586884890031 7613.015779922371
10 4870.277837036473 5219.242211347652 7081.725652944361
Mean 4867.18605 5156.2345 7395.94031

This gives me interesting insight, which pheromone is obviously working.

4. Greedy Algorithm

Greedy algorithm is a way to select currently best value at each step. For this algorithm, the starting node affects very much to the result, so that I randomly choose one node to start. However, you can give an initial node by -i flag.

def greedy_from(n, nodes):
    # 51, 1111492.9231858053 # 1 to 62
    path = [n]
    toVisit = list(nodes.keys())
    toVisit.remove(n)
    while len(toVisit) > 0:
        m = 999999999
        mIdx = -1
        for target in toVisit:
            dist = distanceBtw(nodes[target], nodes[path[-1]])
            if dist < m:
                m = dist
                mIdx = target

        toVisit.remove(mIdx)
        path.append(mIdx)
    return path

4.1 Results

I examined greedy algorithm with random starting node with a280.tsp and att532.tsp.

Trial a280.tsp att532.tsp
1 3296.364589364881 110014.87128448064
2 3417.4756412042807 110089.93383821366
3 3177.506070991219 111004.85477994182
4 3133.7552366506497 109946.67910100473
5 3307.514365995899 107503.32509092095
6 3233.598998788573 110016.81611787807
7 3203.4698583413447 107367.82695412835
8 3506.0688852020426 110917.43418611592
9 3176.204206298816 111022.91002522611
10 3204.2546561621757 105927.10649036754
Mean 3265.62125 109381.176

Compared to the other algorithms using same dataset, greedy algorithm shows much better performance. (even it is very fast!) However, as we all already know, greedy cannot ensure to find global optima.

5. Minimum Spanning Tree

Note that this is deterministic algorithm.

I do not write this algorithm as submission itself, but only used this algorithm for other algorithm, for example, the initial path for genetic algorithm or result maker for ant colony optimization.

I calculated the TSP by adding one simple condition to Prim's algorithm, which is Degrees of all nodes are up to 2. This small modification ensures that the result of modified Prim's algorithm satisfies TSP.

5.1 Results

a280.tsp att532.tsp rl11849.tsp
Result 2960.4783808070056 106018.79430856417 1039269.8361605078

The result is even shorter than greedy algorithm, but this might be also one of local optima.

6. Conclusion

Before I conclude something, it was very sorry not to have enough time to examine very large number of generations. Almost all the evolution algorithm could make good performance under late stage of generations, since the population can be thought as evolved. However, for my case, I cannot spend much time to reach that stage.

As the Results chapter of each algorithm said, the deterministic algorithm performs the best among four algorithms I used. (This is sad). However, As we already know, it is obviously falled in local optima, and this is proved by leaderboard. (I am not on the top) This obviously affects to the ACO algorithm with MST, so ACO with MST gave me the best performance except MST itself.

If I do not consider MST at all, I think the best performance is coming from Genetic Algorithm. The performance here also includes the running time, and GA's running time was much faster than ACO's running time. I think this is because I need to calculate $nAnts \times nGenerations$ for ACO algorithm unlike to GA which only calculates $nGenerations$ times.

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