thefittest 0.2.3

Implementation of data mining methods that use evolutionary algorithms

Thefittest

Installation

pip install thefittest

Dependencies

thefittest requires:

• python (>=3.7,<3.12);
• numpy;
• numba;
• scipy.

The package contains methods

• Genetic algorithm (Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72):
  • Self-configuring genetic algorithm (Semenkin, E.S., Semenkina, M.E. Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator. LNCS, 7331, 2012, pp. 414-421. https://doi.org/10.1007/978-3-642-30976-2_50);
  • SHAGA (Stanovov, Vladimir & Akhmedova, Shakhnaz & Semenkin, Eugene. (2019). Genetic Algorithm with Success History based Parameter Adaptation. 180-187. http://dx.doi.org/10.5220/0008071201800187).
• Differential evolution (Storn, Rainer & Price, Kenneth. (1995). Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. Journal of Global Optimization. 23)
  • jDE (Brest, Janez & Greiner, Sao & Bošković, Borko & Mernik, Marjan & Zumer, Viljem. (2007). Self-Adapting Control Parameters in Differential Evolution: A Comparative 13. 945 - 958. http://dx.doi.org/10.1109/TEVC.2009.2014613);
  • SHADE (Tanabe, Ryoji & Fukunaga, Alex. (2013). Success-history based parameter adaptation for Differential Evolution. 2013 IEEE Congress on Evolutionary Computation, CEC 2013. 71-78. https://doi.org/10.1109/CEC.2013.6557555).
• Genetic programming (Koza, John R.. “Genetic programming - on the programming of computers by means of natural selection.” Complex Adaptive Systems (1993)):
  • Self-configuring genetic programming (Semenkin, Eugene & Semenkina, Maria. (2012). Self-configuring genetic programming algorithm with modified uniform crossover. 1-6. http://dx.doi.org/10.1109/CEC.2012.6256587).
• Genetic programming of neural networks (GPNN) (Lipinsky L., Semenkin E., Bulletin of the Siberian State Aerospace University., 3(10), 22-26 (2006). In Russian);
• Multilayer perceptron trained by evolutionary algorithms (Cotta, Carlos & Alba, Enrique & Sagarna, R. & Larranaga, Pedro. (2002). Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms. http://dx.doi.org/10.1007/978-1-4615-1539-5_18);

Benchmarks

• CEC2005 (Suganthan, Ponnuthurai & Hansen, Nikolaus & Liang, Jing & Deb, Kalyan & Chen, Ying-ping & Auger, Anne & Tiwari, Santosh. (2005). Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Natural Computing. 341-357);
• Symbolicregression17. 17 test regression problem from the paper (Semenkin, Eugene & Semenkina, Maria. (2012). Self-configuring genetic programming algorithm with modified uniform crossover. 1-6. http://dx.doi.org/10.1109/CEC.2012.6256587).
• Iris (Fisher,R. A.. (1988). Iris. UCI Machine Learning Repository. https://doi.org/10.24432/C56C76.);
• Wine (Aeberhard,Stefan and Forina,M.. (1991). Wine. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.);
• Breast Cancer Wisconsin (Diagnostic) (Wolberg,William, Mangasarian,Olvi, Street,Nick, and Street,W.. (1995). Breast Cancer Wisconsin (Diagnostic). UCI Machine Learning Repository. https://doi.org/10.24432/C5DW2B.);
• Optical Recognition of Handwritten Digits (Alpaydin,E. and Kaynak,C.. (1998). Optical Recognition of Handwritten Digits. UCI Machine Learning Repository. https://doi.org/10.24432/C50P49.);

Examples

Notebooks on how to use thefittest:

• Solving binary and real-valued optimization problems with a genetic algorithm;
• Solving real-valued optimization problems with a differential evolution;
• Symbolic regression problems solving using genetic programming algorithm;
• Neural network training using evolutionary algorithms for regression and classification problems;
• Optimization of neural network structure using genetic programming;

If some notebooks are too big to display, you can use NBviewer.