thefittest 0.1.9

Implementation of data mining methods that use evolutionary algorithms

thefittest

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);
  • SHAGA (Stanovov, Vladimir & Akhmedova, Shakhnaz & Semenkin, Eugene. (2019). Genetic Algorithm with Success History based Parameter Adaptation. 180-187. 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):
  • SaDE (Qin, Kai & Suganthan, Ponnuthurai. (2005). Self-adaptive differential evolution algorithm for numerical optimization. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings. 2. 1785-1791. 10.1109/CEC.2005.1554904);
  • jDE (Brest, Janez & Greiner, Sao & Bošković, Borko & Mernik, Marjan & Zumer, Viljem. (2007). Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. Evolutionary Computation, IEEE Transactions on. 10. 646 - 657. 10.1109/TEVC.2006.872133);
  • JADE (Zhang, Jingqiao & Sanderson, A.C.. (2009). JADE: Adaptive Differential Evolution With Optional External Archive. Evolutionary Computation, IEEE Transactions on. 13. 945 - 958. 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. 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. 10.1109/CEC.2012.6256587).

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. 10.1109/CEC.2012.6256587).