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Using the three datasets, we ran a series of 32 runs, using DAO versions v1.10 to v1.15, varying only the cooling schedules and the
dataset. Each round within a term's dataset began with the same
arbitrary placement of all students into available beds, followed by a
randomization step^{}. The objective function penalties remained constant, and
each round used a different random number seed. All students were
moveable and no beds were blocked. A minimization step in priority
order followed each round.
Using nonparametric ranking methods which make no assumptions about
the distribution of the samples, we calculate the mean
rank^{} of various approaches
listed in Table 4.2 based on the ranks of their
measurements rather than the measurements themselves.
Ranking the data from lowest to highest scores, we use the
KruskalWallis test, often called an ``analysis of variance by
ranks'', for groups with three or more sample populations. For tied
ranks, the rank assigned is the mean of the ranks they would have been
assigned had they not been tied.
In each case, the null hypothesis asserts the populations are the
same. If the test statistic is greater than (and in
most cases .01), we reject the null hypothesis, and conclude the
samples come from different populations.
To determine between which of the samples significant differences
occur, multiple comparison tests of the rank means are made against
the test statistic [Zar99].
The following sections present our specific investigations and
observations.
Table 4.5:
Statistics (in millions) from Fall 1998 Tests
Scheme 
Avg Score 
Std Dev 
Avg Best

Std Dev 
Avg Attempts 

sa0 
49.084 
0.270 
49.082 
0.276 
54.106 

sa1 
52.170 
0.887 
49.814 
0.303 
6.340 

sa2 
49.276 
0.324 
49.174 
0.209 
12.095 

sa3 
50.062 
0.620 
49.284 
0.258 
8.171 

sa4 
49.069 
0.308 
49.055 
0.288 
19.528 

sigmet4 
49.051 
0.235 
49.032 
0.236 
18.605 

sigmet1 
52.022 
0.857 
49.741 
0.366 
6.162 

rldhc 
49.121 
0.244 
49.115 
0.248 
42.526 

mr 
667.506 
10.266 
667.506 
10.266 
40.000 

m 
671.013 
7.197 
671.013 
7.197 
40.000 

r 
933.03 
11.232 
933.03 
11.232 
.100 

Table 4.3:
Statistics (in millions) from Fall 2000 Tests
Scheme 
Avg Score 
Std Dev 
Avg Best

Std Dev 
Avg Attempts 
sa0 
88.685 
0.188 
88.683 
0.194 
43.376 
sa1 
92.640 
0.872 
89.306 
0.238 
8.379 
sa2 
88.858 
0.292 
88.777 
0.226 
17.735 
sa3 
89.281 
0.498 
88.722 
0.189 
13.019 
sa4 
88.629 
0.243 
88.620 
0.229 
27.608 
sigmet4 
88.570 
0.214 
88.562 
0.213 
26.884 
sigmet1 
92.998 
1.015 
89.362 
0.256 
8.482 
rldhc 
88.750 
0.221 
88.743 
0.218 
43.361 
mr 
867.751 
8.405 
867.751 
8.405 
40.000 
m 
863.249 
7.132 
863.249 
7.132 
40.000 
r 
1156.37 
35.924 
1156.37 
35.924 
.100 
Table 4.4:
Statistics (in millions) from Fall 1999 Tests
Scheme 
Avg Score 
Std Dev 
Avg Best

Std Dev 
Avg Attempts 
sa0 
71.336 
0.831 
71.329 
0.839 
44.162 
sa1 
75.310 
1.080 
72.049 
0.881 
9.182 
sa2 
71.078 
0.520 
71.062 
0.514 
19.830 
sa3 
71.606 
0.895 
71.268 
0.760 
14.021 
sa4 
71.193 
0.805 
71.173 
0.803 
31.728 
sigmet4 
71.077 
0.740 
71.071 
0.734 
31.255 
sigmet1 
74.810 
1.209 
71.788 
0.807 
9.102 
rldhc 
70.992 
0.544 
70.987 
0.533 
43.772 
mr 
836.634 
5.462 
836.634 
5.462 
40.000 
m 
838.684 
5.437 
838.684 
5.437 
40.000 
r 
1125.95 
13.412 
1125.95 
13.412 
.100 

Next: Random Optimization
Up: Experimental Results
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Contents
elena s ackley
20020120
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