output.bipolar.90.fitness

PostsBayesian Optimization Algorithm with Decision Graphs in C++,

  0    2700  13.599998  10.290408   4.900001
  1    4050  13.599998  10.919482   6.700001
  2    5400  13.599998  11.175149   6.200000
  3    6750  13.599998  11.476741   6.600000
  4    8100  13.599998  11.818815   7.700000
  5    9450  13.599998  12.137667   8.600000
  6   10800  13.599998  12.423815   9.900001
  7   12150  13.599998  12.732593  10.800000
  8   13500  13.599999  12.857111  10.900001
  9   14850  13.899999  12.935444  10.700001
 10   16200  13.900000  13.036740  11.200000
 11   17550  13.900000  13.172703  12.300001
 12   18900  13.900000  13.162962  11.100000
 13   20250  13.900000  13.188481  10.599999
 14   21600  14.099999  13.339333  11.400000
 15   22950  14.299999  13.481184  12.300000
 16   24300  14.299999  13.566110  12.299999
 17   25650  14.400000  13.687406  12.900000
 18   27000  14.599999  13.767851  12.099999
 19   28350  14.599999  13.728332  10.999999
 20   29700  14.599999  13.830851  11.700000
 21   31050  14.599999  14.033184  12.799999
 22   32400  14.799999  14.153036  13.400000
 23   33750  14.800000  14.253629  12.999999
 24   35100  14.900000  14.377925  13.799999
 25   36450  14.900000  14.427222  12.500000
 26   37800  15.000000  14.572851  13.999999
 27   39150  15.000000  14.668444  14.199999
 28   40500  15.000000  14.755407  14.199999
 29   41850  15.000000  14.838148  13.700000
 30   43200  15.000000  14.906444  13.800000
 31   44550  15.000000  14.957111  14.700000
 32   45900  15.000000  15.000000  15.000000