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Analysis of the use of genetic algorithms for indoor localisation via cloud point matching

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Figure

Figure 1: Fitness landscapes for randomly selected scans indataset.
Figure 2: Efects of sub-sampling on features in the refer-ence map, with tolerances of 0.1, 0.2 & 0.5 units respectively.
Figure 5: Efects of varied mutation sizes on performance ofthe benchmark algorithm.
Figure 7: Performance of each algorithm with 50 population,as many generations as possible within the time frame andoptimal parameters for each algorithm.
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