_PROBLEM CoEPrA-2006_Regression_002

_GROUP_NAME Efstratios Georgopoulos

_GROUP_MEMBERS
Efstratios Georgopoulos,
 Technological Educational Institute of Kalamatas
 Assistant Professor
 and Hellenic Open University Computer Science Dept Tutor,
 sfg@teikal.gr

George Zarogiannis,
 Hellenic Open University
 Computer Science Dept
 student,
 zarogiannis@gmail.com

_ADDRESS
Dr. Efstratios F. Georgopoulos,
Technological Educational Institute of Kalamatas,
24100, Kalamata, Greece.
tel.: +302721045278

_MODELING_PROCEDURE
Proposed software model developed by Tree-Based Genetic Programming
 introduced by John R. Koza. This machine learning paradigm uses
 genetic algorithm to evolve tree structures that represent computer
 programs. For an introduction, you may see http://www.genetic-programming.org/.
 Steady state approach with tournament selection preferred against the
 use of generations, for performance optimization. All original descriptors
 without any modification provided as input to the algorithm. Descriptors
 selected to appear in the final model by the overall evolutionary process.
 Four different training - validation datasets created and four different
 runs initiated using these sets. Solutions populations resulted by all
 algorithm runs unified and a run initiated using complete calibration
 dataset for evaluation. Best model of this run scored 0.00096 Mean
 Squared Error and used for prediction.

_PREDICTION
Obj_00001 7.554
Obj_00002 7.693
Obj_00003 8.001
Obj_00004 7.916
Obj_00005 7.768
Obj_00006 8.249
Obj_00007 8.540
Obj_00008 7.926
Obj_00009 6.503
Obj_00010 5.970
Obj_00011 7.913
Obj_00012 6.322
Obj_00013 7.551
Obj_00014 7.693
Obj_00015 7.653
Obj_00016 7.753
Obj_00017 7.938
Obj_00018 6.823
Obj_00019 7.457
Obj_00020 7.813
Obj_00021 7.276
Obj_00022 7.737
Obj_00023 7.846
Obj_00024 7.414
Obj_00025 5.196
Obj_00026 8.024
Obj_00027 7.796
Obj_00028 8.030
Obj_00029 8.325
Obj_00030 8.088
Obj_00031 7.243
Obj_00032 7.788
Obj_00033 7.712
Obj_00034 7.872
Obj_00035 7.567
Obj_00036 8.208
Obj_00037 8.213
Obj_00038 8.023
Obj_00039 7.890
Obj_00040 5.970
Obj_00041 8.056
Obj_00042 7.392
Obj_00043 7.946
Obj_00044 7.759
Obj_00045 7.837
Obj_00046 8.514
Obj_00047 6.330
Obj_00048 7.971
Obj_00049 8.079
Obj_00050 5.121
Obj_00051 8.341
Obj_00052 8.356
Obj_00053 6.904
Obj_00054 8.201
Obj_00055 7.590
Obj_00056 6.472
Obj_00057 7.912
Obj_00058 7.026
Obj_00059 7.266
Obj_00060 7.936
Obj_00061 7.859
Obj_00062 8.397
Obj_00063 8.420
Obj_00064 7.904
Obj_00065 5.426
Obj_00066 7.853
Obj_00067 7.804
Obj_00068 6.202
Obj_00069 7.422
Obj_00070 8.126
Obj_00071 8.150
Obj_00072 7.859
Obj_00073 8.327
Obj_00074 7.919
Obj_00075 7.831
Obj_00076 5.405