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Changes between Version 20 and Version 21 of UsersSamples


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Timestamp:
02/18/11 13:19:57 (13 years ago)
Author:
gkronber
Comment:

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  • UsersSamples

    v20 v21  
    251251=== Genetic programming - Symbolic Regression (Tower)===
    252252[attachment:SGP_SymbReg.hl]
    253 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.
     253
     254Example 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
     256 * Population size: 500
     257 * Generations: 100
     258 * Tournament selection: group size=7
     259 * Mutation rate: 15%
     260 * Function set: +, *, -, /, average, log, power, root, exponential
     261 * Terminal set: constants, x01, x02, x03, x04, x05, x06, x08, x09, x10, x12, x14, x15, x17, x18, x19, x20, x22, x23, x24
     262 * Fitness function: R² of predicted and original tower response
    254263
    255264----
     
    257266[=#GPSymbolicRegressionMackeyGlass]
    258267=== Genetic programming - Symbolic Regression (Mackey-Glass)===
     268[attachement:SGP_SymbReg-Mackey-Glass.hl]
     269
     270Example for a simple genetic programming algorithm to create a dynamic model for the one-step prediction of the chaotic Mackey Glass (T=17) time series. The original dataset was downloaded from http://www.bme.ogi.edu/~ericwan/data.html.
     271
     272 * Population size: 500
     273 * Generations: 100
     274 * Tournament selection: group size=7
     275 * Mutation rate: 15%
     276 * Function set: +, *, -, /, average, log, power, root, exponential
     277 * Terminal set: constants, x(t-90) .. x(t-1)
     278 * Fitness function: R²(xPred(t), xOrig(t))
     279
     280
    259281----
    260282
     
    262284=== Genetic programming - Symbolic Classification (Wisconsin)===
    263285[attachment:SGP_Classification-WDPC.hl]
     286
    264287Example 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.
    265288
     
    271294=== Genetic programming - Symbolic Classification (Mammography)===
    272295[attachment:SGP_SymbClass-Mammographic.hl]
     296
    273297 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).
    274298
     
    278302=== Genetic Programming - Even Parity (4 inputs) ===
    279303[attachment:SGP_Boolean-Even-Parity-4.hl]
     304
    280305 A genetic programming algorithm to synthesize the boolean even parity function with 4 binary inputs
    281306
     
    297322=== Genetic Programming - Multiplexer (11 inputs ===
    298323[attachment:SGP_Boolean-Multiplexer-11.hl]
     324
    299325 A genetic programming algorithm to synthesize the boolean multiplexer function with 11 binary inputs
    300326