Changes between Version 19 and Version 20 of UsersSamples

Timestamp:

02/18/11 12:56:01 (13 years ago)

Author:

gkronber

Comment:

added descriptions for GP samples

UsersSamples

@@ -222 +222 @@
 [=#GPSymbolicRegressionBostonHousing]
 === Genetic programming - Symbolic Regression (Boston Housing)===
+[attachment:SGP_SymbReg-Boston-Housing.hl]
+
+Example for a simple genetic programming algorithm to create a regression model for the estimation of the median value of houses in a certain in the Boston area based on other parameters of that region. The original dataset was downloaded from http://archive.ics.uci.edu/ml/datasets/Housing. 
 
 '''Algorithm:''' [[Genetic Algorithm]] 
@@ -247 +250 @@
 [=#GPSymbolicRegressionTower]
 === Genetic programming - Symbolic Regression (Tower)===
+[attachment:SGP_SymbReg.hl]
+Example for a simple genetic programming algorithm to create a regression model for the estimation of a product quality parameter in an industrial chemical process. The original dataset was downloaded from http://vanillamodeling.com/realproblems.html. 
+
 ----
 
@@ -255 +261 @@
 [=#GPSymbolicClassificationWisconsin]
 === Genetic programming - Symbolic Classification (Wisconsin)===
-Example for a simple genetic programming algorithm including the Wisconsin breast cancer dataset from the UCI Machine Learning Repository. The engine uses all available GP manipulation operators, single-point cross-over and squared Pearson's correlation coefficient evaluation.
-
-The engine reaches an accuracy of ~95% on the test set.
-This engine is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models.
+[attachment:SGP_Classification-WDPC.hl]
+Example for a simple genetic programming algorithm to create a classification model for the estimation of malignant or benign tumor diagnosis based on features extracted through analysis of tumorous cells in a tissue sample. The original dataset was downloaded from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). The algorithm uses all available GP manipulation operators, single-point cross-over and squared Pearson's correlation coefficient evaluation.
+
+The algorithm reaches an accuracy of ~95% on the test set.
+This algorithm is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models.
 ----
 
 [=#GPSymbolicRegressionMammography]
 === Genetic programming - Symbolic Classification (Mammography)===
+[attachment:SGP_SymbClass-Mammographic.hl]
+ A genetic programming algorithm to create a classification model for the prediction of malignant or benign tumor diagnosis based on features extracted through a non-invasive mammography breast cancer screening. Original dataset stems from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass).
+
 ----
 
@@ -297 +307 @@
 John R. Koza, Genetic Programming - On the Programming of Computers by Means of Natural Selection, MIT Press, 1992
 
+----
 == Additional ==