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, DEAP is an open source project under an LGPL license.

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Francesco Biscani; Dario Izzo: A parallel global multiobjective framework for optimization: pagmo (2020) not zbMATH
  2. Paulo Paneque Galuzio, Emerson Hochsteiner de Vasconcelos Segundo, Leandro dos Santos Coelho, Viviana Cocco Mariani: MOBOpt - multi-objective Bayesian optimization (2020) not zbMATH
  3. Ruehle, Fabian: Data science applications to string theory (2020)
  4. Sohrab Towfighi: pyGOURGS - global optimization of n-ary tree representable problems using uniform random global search (2020) not zbMATH
  5. Toutouh, Jamal; Rossit, Diego; Nesmachnow, Sergio: Soft computing methods for multiobjective location of garbage accumulation points in smart cities (2020)
  6. Gustavo H. de Rosa, João P. Papa: Opytimizer: A Nature-Inspired Python Optimizer (2019) arXiv
  7. Markus Quade; Julien Gout; Markus Abel: Glyph: Symbolic Regression Tools (2019) not zbMATH
  8. Pelamatti, Julien; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Guerin, Yannick: Efficient global optimization of constrained mixed variable problems (2019)
  9. Pereira, Eduardo S.; Santos, Pedro A.; Velho, Haroldo F. De Campos: Towards the super-massive black hole seeds (2019)
  10. Frassetto, Erin; Gableman, Michael; Lamb, McKenzie; Shimek, Tyler; Young, Andrea: Strange spinners and diversity of dice in chutes and ladders (2018)
  11. Salman, Sinan; Alaswad, Suzan: Alleviating road network congestion: traffic pattern optimization using Markov chain traffic assignment (2018)
  12. Krawiec, Krzysztof; Liskowski, Paweł: Adaptive test selection for factorization-based surrogate fitness in genetic programming (2017)
  13. Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly: Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning (2015) not zbMATH
  14. Sun, Jun; Wu, Xiaojun; Palade, Vasile; Fang, Wei; Shi, Yuhui: Random drift particle swarm optimization algorithm: convergence analysis and parameter selection (2015)
  15. Lara-Cabrera, Raúl; Cotta, Carlos; Fernández-Leiva, Antonio: On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms (2014) ioport
  16. Reina, D. G.; León-Coca, J. M.; Toral, S. L.; Asimakopoulou, E.; Barrero, F.; Norrington, P.; Bessis, N.: Multi-objective performance optimization of a probabilistic similarity/dissimilarity-based broadcasting scheme for mobile ad hoc networks in disaster response scenarios (2014) ioport
  17. Fortin, Félix-Antoine; De Rainville, François-Michel; Gardner, Marc-André; Parizeau, Marc; Gagné, Christian: DEAP: evolutionary algorithms made easy (2012) ioport