# Hw5 - 1i Boxplots of Data 500 450 400 350 Cos t 300 250 200 150 100 50 0 SA GA Algorithm GS Assuming we are trying to minimize cost SA has the

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1i. 0 50 100 150 200 250 300 350 400 450 500 SA GA GS Algorithm Boxplots of Data Cost Assuming we are trying to minimize cost, SA has the lowest mean while GA has the highest mean. SA and GS have very similar variances, while GA has an extremely high variance. GA found the lowest solution (within each trial) on multiple occasions, but it was very erratic, thus the high variance. The boxplot shows one outlier for the GA data, a value of 488.83. In our opinion, the SA algorithm performed the best because it had the lowest mean and a standard deviation very close to that of GS. Additionally, the best value SA found is better than the best for GA and GS and the WORST value SA found is better than the worst for GA and GS. While the GS had a very low standard deviation, its mean was more than 50% higher than that of SA.

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1ii. 0 50 100 150 200 250 300 350 400 450 500 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Empirical CDFs Cost Cumulative Probability SA GA GS The empirical CDFs above show SA has the highest probability of having any
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## This note was uploaded on 11/29/2011 for the course ORIE 5140 taught by Professor Shoemaker during the Fall '11 term at Cornell University (Engineering School).

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Hw5 - 1i Boxplots of Data 500 450 400 350 Cos t 300 250 200 150 100 50 0 SA GA Algorithm GS Assuming we are trying to minimize cost SA has the

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