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 27 articles , 1 standard article )

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  1. Chathika Gunaratne, Ivan Garibay: NL4Py: Agent-based modeling in Python with parallelizable NetLogo workspaces (2021) not zbMATH
  2. 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)
  3. Luca Demetrio, Battista Biggio: secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers (2021) arXiv
  4. Müller, Juliane; Park, Jangho; Sahu, Reetik; Varadharajan, Charuleka; Arora, Bhavna; Faybishenko, Boris; Agarwal, Deborah: Surrogate optimization of deep neural networks for groundwater predictions (2021)
  5. Bigoni, Caterina; Zhang, Zhenying; Hesthaven, Jan S.: Systematic sensor placement for structural anomaly detection in the absence of damaged states (2020)
  6. Bose, Amarnath: Using genetic algorithm to improve consistency and retain authenticity in the analytic hierarchy process (2020)
  7. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  8. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  9. Paulo Paneque Galuzio, Emerson Hochsteiner de Vasconcelos Segundo, Leandro dos Santos Coelho, Viviana Cocco Mariani: MOBOpt - multi-objective Bayesian optimization (2020) not zbMATH
  10. Ruehle, Fabian: Data science applications to string theory (2020)
  11. Sohrab Towfighi: pyGOURGS - global optimization of n-ary tree representable problems using uniform random global search (2020) not zbMATH
  12. Toutouh, Jamal; Rossit, Diego; Nesmachnow, Sergio: Soft computing methods for multiobjective location of garbage accumulation points in smart cities (2020)
  13. Trejo-Sánchez, Joel Antonio; Fajardo-Delgado, Daniel; Gutierrez-Garcia, J. Octavio: A genetic algorithm for the maximum 2-packing set problem (2020)
  14. Vidnerová, Petra; Neruda, Roman: Vulnerability of classifiers to evolutionary generated adversarial examples (2020)
  15. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  16. Gustavo H. de Rosa, João P. Papa: Opytimizer: A Nature-Inspired Python Optimizer (2019) arXiv
  17. Markus Quade; Julien Gout; Markus Abel: Glyph: Symbolic Regression Tools (2019) not zbMATH
  18. Pelamatti, Julien; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Guerin, Yannick: Efficient global optimization of constrained mixed variable problems (2019)
  19. Pereira, Eduardo S.; Santos, Pedro A.; Velho, Haroldo F. De Campos: Towards the super-massive black hole seeds (2019)
  20. Frassetto, Erin; Gableman, Michael; Lamb, McKenzie; Shimek, Tyler; Young, Andrea: Strange spinners and diversity of dice in chutes and ladders (2018)

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