Changes between Version 25 and Version 26 of UsersSamples
 Timestamp:
 03/04/11 09:20:00 (12 years ago)
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UsersSamples
v25 v26 223 223 === Genetic programming  Symbolic Regression (Boston Housing)=== 224 224 [export:/misc/samples/GP_RegressionBostonHousing.hl GP_RegressionBostonHousing.hl] 225 [[Image(GP_BostonHousingscreenshot.png, width=500, right, marginright=30, marginleft=30)]] 225 226 226 227 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. … … 251 252 === Genetic programming  Symbolic Regression (Tower)=== 252 253 [export:/misc/samples/GP_RegressionTowerResponse.hl GP_RegressionTowerResponse.hl] 254 [[Image(GP_TowerResponsescreenshot.png, width=500, right, marginright=30, marginleft=30)]] 253 255 254 256 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. … … 267 269 === Genetic programming  Symbolic Regression (MackeyGlass)=== 268 270 [export:/misc/samples/GP_RegressionMackeyGlass.hl GP_RegressionMackeyGlass.hl] 271 [[Image(GP_MackeyGlassscreenshot.png, width=500, right, marginright=30, marginleft=30)]] 269 272 270 273 Example for a simple genetic programming algorithm to create a dynamic model for the onestep prediction of the chaotic Mackey Glass (T=17) time series. The original dataset was downloaded from http://www.bme.ogi.edu/~ericwan/data.html. … … 284 287 === Genetic programming  Symbolic Classification (Wisconsin)=== 285 288 [export:/misc/samples/GP_ClassificationWisconsinDiagnostic.hl GP_ClassificationWisconsinDiagnostic.hl] 289 [[Image(GP_WisconsinDiagnosticscreenshot.png, width=500, right, marginright=30, marginleft=30)]] 286 290 287 291 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, singlepoint crossover and squared Pearson's correlation coefficient evaluation. … … 289 293 The algorithm reaches an accuracy of ~95% on the test set. 290 294 This algorithm is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models. 295 296 [[br]] 297 [[br]] 298 [[br]] 299 [[br]] 300 [[br]] 301 [[br]] 302 [[br]] 303 [[br]] 304 [[br]] 305 [[br]] 306 [[br]] 307 [[br]] 308 [[br]] 309 [[br]] 310 [[br]] 311 [[br]] 291 312  292 313 … … 294 315 === Genetic programming  Symbolic Classification (Mammography)=== 295 316 [export:/misc/samples/GP_ClassificationMammography.hl GP_ClassificationMammography.hl] 317 [[Image(GP_Mammographyscreenshot.png, width=500, right, marginright=30, marginleft=30)]] 296 318 297 319 A genetic programming algorithm to create a classification model for the prediction of malignant or benign tumor diagnosis based on features extracted through a noninvasive mammography breast cancer screening. Original dataset stems from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass).