DEAP

DEAP: evolutionary algorithms made easy. DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black-box frameworks. Freely available with extensive documentation at http://deap.gel.ulaval.ca, DEAP is an open source project under an LGPL license.


References in zbMATH (referenced in 33 articles , 1 standard article )

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  1. Shin, Hyelim; Lee, Taesik; Lee, Hyun-Rok: Skyport location problem for urban air mobility system (2022)
  2. Ahmed Fawzy Gad: PyGAD: An Intuitive Genetic Algorithm Python Library (2021) arXiv
  3. Aksakalli, Vural; Yenice, Zeren D.; Malekipirbazari, Milad; Kargar, Kamyar: Feature selection using stochastic approximation with Barzilai and Borwein non-monotone gains (2021)
  4. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  5. Demo, Nicola; Ortali, Giulio; Gustin, Gianluca; Rozza, Gianluigi; Lavini, Gianpiero: An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (2021)
  6. Luca Demetrio, Battista Biggio: secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers (2021) arXiv
  7. Müller, Juliane; Park, Jangho; Sahu, Reetik; Varadharajan, Charuleka; Arora, Bhavna; Faybishenko, Boris; Agarwal, Deborah: Surrogate optimization of deep neural networks for groundwater predictions (2021)
  8. Roman, Ibai; Santana, Roberto; Mendiburu, Alexander; Lozano, Jose A.: Evolution of Gaussian process kernels for machine translation post-editing effort estimation (2021)
  9. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  10. Adhao, Rahul; Pachghare, Vinod: Feature selection using principal component analysis and genetic algorithm (2020)
  11. Bigoni, Caterina; Zhang, Zhenying; Hesthaven, Jan S.: Systematic sensor placement for structural anomaly detection in the absence of damaged states (2020)
  12. Bose, Amarnath: Using genetic algorithm to improve consistency and retain authenticity in the analytic hierarchy process (2020)
  13. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  14. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  15. Paulo Paneque Galuzio, Emerson Hochsteiner de Vasconcelos Segundo, Leandro dos Santos Coelho, Viviana Cocco Mariani: MOBOpt - multi-objective Bayesian optimization (2020) not zbMATH
  16. Ruehle, Fabian: Data science applications to string theory (2020)
  17. Sohrab Towfighi: pyGOURGS - global optimization of n-ary tree representable problems using uniform random global search (2020) not zbMATH
  18. Toutouh, Jamal; Rossit, Diego; Nesmachnow, Sergio: Soft computing methods for multiobjective location of garbage accumulation points in smart cities (2020)
  19. Trejo-Sánchez, Joel Antonio; Fajardo-Delgado, Daniel; Gutierrez-Garcia, J. Octavio: A genetic algorithm for the maximum 2-packing set problem (2020)
  20. Vidnerová, Petra; Neruda, Roman: Vulnerability of classifiers to evolutionary generated adversarial examples (2020)

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