thefittest 0.2.3
Implementation of data mining methods that use evolutionary algorithms
Requires Python: <3.12,>=3.7

# Thefittest 

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## Installation
```bash
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;**](https://github.com/sherstpasha/thefittest-notebooks/blob/main/genetic_algorithm_binary_rastrigin_custom_problems.ipynb) 
*   [**Solving real-valued optimization problems with a differential evolution;**](https://github.com/sherstpasha/thefittest-notebooks/blob/main/differential_evolution_griewank_custom_problems.ipynb) 
*   [**Symbolic regression problems solving using genetic programming algorithm;**](https://github.com/sherstpasha/thefittest-notebooks/blob/main/genetic_programming_symbolic_regression_problem.ipynb)
*   [**Neural network training using evolutionary algorithms for regression and classification problems;**](https://github.com/sherstpasha/thefittest-notebooks/blob/main/mlpea_regression_classification_problem.ipynb)
*   [**Optimization of neural network structure using genetic programming;**](https://github.com/sherstpasha/thefittest-notebooks/blob/main/gpnn_regression_classification_problems.ipynb)

 **If some notebooks are <u>too big to display</u>, you can use [<u>NBviewer</u>](https://nbviewer.org/)**.