Mathematical Problems in Engineering / 2012 / Article / Tab 9

Research Article

Goal-Programming-Driven Genetic Algorithm Model for Wireless Access Point Deployment Optimization

Table 9

Analysis of the results of Experiment  3.

IndicatorFixed mutation rate ( )Fixed crossover rate ( )

Fitness1 (.9869)1 ( )1 ( )1 ( )1 ( )
Generation96 (93)53 (86)18 (26)9 (94)22 (62)
Time489.2550
(3795.2)
526.2310
(5080.5)
552.5640
(3880.6)
496.0540
(3334.8)
541.5390
(4866.5)
Cost1,349,670
(5,736,480)
1,326,549
(5,593,797)
1,347,933
(5,576,205)
1,348,896
(5,580,897)
1,324,479
(5,566,053)
Capacity fulfillment rate.8485
(.8641)
.8384
(.8536)
.8495
(.8486)
.8450
(.8502)
.8531
(.8560)
Coverage fulfillment rate.8500
(.8536)
.8496
(.8634)
.8484
(.8506)
.8496
(.8510)
.8496
(.8559)
Interference.8996
(.8929)
.9100
(.8914)
.8996
(.8891)
.9048
(.8987)
.9012
(.8952)

The experiment results for E3(b) are in parentheses.

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