series #4

16 Aug 2012
run length:20 generations
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
max 1.061071 1.104874 1.154681 1.00908 1.219989 1.256767 1.644667 1.797065 1.856272 1.896181 1.895014 1.934326 1.854957 1.892931 2.01503 1.78847 1.859969 1.980477 1.738598 2.033927
max 1.848776 1.962595 1.835533 2.01698 1.88434 1.789732 1.863232 1.929178 1.862737 1.859898 1.73998 1.981866 1.879812 1.926405 1.881105 1.953121 1.802464 1.821781 1.863731 1.886666
max 1.803367 1.863191 1.83923 1.88361 1.802582 1.947608 1.86119 1.895978 1.832229 1.552671 1.835665 1.847514 1.926618 1.855998 1.923515 1.945651 1.835711 1.711314 1.835681 1.759734
max 1.817896 1.863188 1.874401 1.977176 1.912029 1.979559 1.833231 1.82655 1.850766 1.881107 1.929451 1.900145 1.864397 1.945694 1.978219 1.98038 1.998105 1.764013 1.80258 1.904959
max 1.968566 1.819364 1.904451 1.985371 1.83243 1.848051 1.788508 1.85422 1.944244 1.764048 1.980032 1.962476 1.848117 1.832182 1.835709 1.833513 1.795248 1.459448 1.852226 1.817672
max 1.83423 1.966339 1.82186 1.898968 1.864949 1.895842 1.947951 1.958565 1.87503 1.930192 1.963275 1.835648 1.980454 1.917502 1.966249 1.929392 1.940081 1.878891 1.869869 1.981437
max 1.898626 1.963309 1.997967 1.893892 1.779686 1.945719 1.948297 1.76697 1.895944 1.967061 1.966405 1.84314 1.911705 1.769794 2.016716 2.035856 1.752227 1.963292 1.907378 1.930107
max 2.014727 1.962295 1.869538 1.833615 1.945689 1.806308 1.951652 1.911965 1.812958 1.930143 1.896327 1.836903 1.941074 1.918335 1.848204 1.746346 1.764039 1.916694 1.830149 1.817358
max 1.751192 1.826191 2.03419 1.93392 1.765089 1.693318 1.85545 1.856303 1.912506 1.89779 2.002419 1.883951 1.843145 1.792467 1.779684 1.771044 1.795926 1.819408 1.864353 1.980579
max 1.884097 1.958208 1.821199 1.838844 1.820849 1.706103 1.872355 1.747292 1.795959 1.830387 1.792558 1.892862 1.84652 1.968264 1.91401 1.975613 1.732996 1.833388 1.860215 1.848153
Population Size
200
Run Length
20
Do Training
False
Do Hybrid Training
True
Do Competitive Run
False
Do Adaptive Training
False
Inputs
hasTarget,targetLeft,targetRight,targetNorth,targetSouth,dirToTarget,distToTarget,wall,sensor0,sensor1,sensor2
Outputs
left,right,up,down,run,sensordir0,sensordir1,sensordir2
Hidden Layers
8 12 12 12 8
Back Propogation
True
Learning rate
0.5
Momentum
0.1
Growth rate
0.5

testing...

14 Aug 2012
run length:5 generations
0 1 2 3 4
max 0.1658566 0.8550526 0.8095916 0.8118564 0.62404
max 0.774308 1.050156 1.067261 0.9588463 1.0517
max 0.9334397 0.6263915 0.9628677 0.6625569 0.7656832
max 1.112449 0.8525104 0.6623848 0.875803 1.004091
Population Size
100
Run Length
5
Do Training
False
Do Hybrid Training
True
Do Competitive Run
False
Do Adaptive Training
True
Inputs
hasTarget,targetLeft,targetRight,targetNorth,targetSouth,dirToTarget,distToTarget,wall,sensor0,sensor1,sensor2
Outputs
left,right,up,down,run,sensordir0,sensordir1,sensordir2
Hidden Layers
8 12 12 12 8
Back Propogation
True
Learning rate
0.5
Momentum
0.1
Growth rate
0.5

series #3

14 Aug 2012
run length:20 generations
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
max 1.060326 0.9219891 1.197832 1.235663 1.760151 2.547854 2.280119 2.709229 3.110211 3.032911 3.200301 3.290977 3.289626 3.724942 3.397713 4.531785 3.687705 5.12509 3.838051 5.372205
max 4.365633 5.541535 4.631602 5.611127 5.123827 5.780396 5.211282 5.857319 5.401208 6.027156 5.534879 6.696008 6.027545 7.241452 6.287432 7.425541 6.461873 7.529197 6.595923 7.598262
max 6.915073 7.731515 7.048834 7.889269 7.118394 8.04704 7.28994 8.969346 7.906232 9.106852 8.063957 9.171782 8.238565 9.305981 8.413652 9.48721 8.523644 9.661653 8.60246 9.811969
max 9.325891 9.986445 9.748722 10.12824 9.836178 10.30925 9.903879 10.37875 10.02561 10.47422 10.10949 10.97528 10.21808 11.05671 10.31677 11.17018 10.48764 11.26284 10.66264 11.43793
max 10.75192 11.51774 10.84278 11.66647 11.848 11.75579 11.98168 11.87542 12.052 12.0499 12.14131 12.13922 12.27414 12.74827 12.46897 13.41667 12.54482 13.5563 12.6496 13.64054
max 12.85152 13.78234 13.00398 13.85808 13.07867 13.95087 13.20257 14.08911 13.29661 14.22711 13.49623 14.30381 14.26102 14.43881 14.3248 14.61172 14.80777 15.25174 14.94144 15.34696
max 15.45169 15.79852 16.33741 15.9592 16.41856 16.12408 16.5515 16.93718 16.68687 17.01241 16.84572 17.17001 16.97939 17.24471 17.14441 17.31942 17.27552 17.41382 17.58356 17.50115
max 17.67101 17.6391 17.76541 17.85219 17.85274 17.94658 17.97777 18.04054 18.17578 18.21582 18.31079 18.653 18.60208 18.72836 18.75978 18.82127 18.93426 19.26012 19.06805 19.39569
max 19.77956 19.52219 20.12265 19.65586 20.20008 19.72819 20.72746 20.03602 20.92493 20.82488 21.56332 21.28687 21.7378 21.45111 21.91289 21.6262 22.03245 21.77857 22.48543 21.9432
max 22.56078 22.07687 22.65609 22.21054 22.82708 22.47559 23.00217 22.65068 23.10012 22.77138 23.17488 23.84859 23.27261 24.56442 23.48495 24.68566 24.0652 24.77652 24.13993 24.8733
Population Size
200
Run Length
20
Do Training
False
Do Hybrid Training
True
Do Competitive Run
False
Do Adaptive Training
False
Inputs
hasTarget,targetLeft,targetRight,targetNorth,targetSouth,dirToTarget,distToTarget,wall,sensor0,sensor1,sensor2
Outputs
left,right,up,down,run,sensordir0,sensordir1,sensordir2
Hidden Layers
8 12 12 12 8
Back Propogation
True
Learning rate
0.5
Momentum
0.1
Growth rate
0.5