Computational Intelligence ToolKit (CITK)

This is an ultimate package for SOTA CI algorithmes

Installation

git clone https://github.com/tupoylogin/Neural_Net_Genetic_Alg.git

cd Neural_Net_Genetic_Alg

pip install . (or pip install -e . to enable edit mode)

Examples

• Multilayer Perceptron trained with Genetic Algorithm
• Multilayer Perceptron trained with SGD
• Multilayer Perceptron trained with Genetic Algorithm and then with SGD
• Multilayer Perceptron trained with Conjugate SGD
• ANFIS Neural Net trained with SGD
• GroupedMethod+Dense Neural Net with Layer Hypersearch trained with SGD
• GMDH Neural Net with batching on LSM
• Fuzzy GMDH Neural Net with batching on Linear Programming
• GMDH Neural Net on LSM on Time Series data
• Fuzzy GMDH Neural Net on Linear Programming on Time Series data

Result Table

All experiments are carried on Boston dataset

Using such preprocessing:

• Quantile Transform on Target (n_quantiles=300, output_distribution="normal")
• Standard Scaling of features

Test/Train splitting:

• test size - 20%
• use histogram bins stratification

Data preparation code

Metric - MSE on normalized data

Experiment name	Train score	Test score
MLP+Genetic	0.508	0.746
MLP+SGD	0.233	0.669
MLP+(Genetic->SGD)	0.244	0.636
MLP+Conjugate SGD	0.307	0.650
ANFIS+SGD	0.561	0.759
GroupedMethod+Dense+SGD+LayerSearch	0.193	0.344
GMDH	0.732	0.423
FuzzyGMDH	94.0	0.432