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 11 articles , 1 standard article )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Gustavo H. de Rosa, João P. Papa: Opytimizer: A Nature-Inspired Python Optimizer (2019) arXiv
  2. Markus Quade; Julien Gout; Markus Abel: Glyph: Symbolic Regression Tools (2019) not zbMATH
  3. Pelamatti, Julien; Brevault, Loïc; Balesdent, Mathieu; Talbi, El-Ghazali; Guerin, Yannick: Efficient global optimization of constrained mixed variable problems (2019)
  4. Frassetto, Erin; Gableman, Michael; Lamb, McKenzie; Shimek, Tyler; Young, Andrea: Strange spinners and diversity of dice in chutes and ladders (2018)
  5. Salman, Sinan; Alaswad, Suzan: Alleviating road network congestion: traffic pattern optimization using Markov chain traffic assignment (2018)
  6. Krawiec, Krzysztof; Liskowski, Paweł: Adaptive test selection for factorization-based surrogate fitness in genetic programming (2017)
  7. 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
  8. Sun, Jun; Wu, Xiaojun; Palade, Vasile; Fang, Wei; Shi, Yuhui: Random drift particle swarm optimization algorithm: convergence analysis and parameter selection (2015)
  9. Lara-Cabrera, Raúl; Cotta, Carlos; Fernández-Leiva, Antonio: On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms (2014) ioport
  10. 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
  11. Fortin, Félix-Antoine; De Rainville, François-Michel; Gardner, Marc-André; Parizeau, Marc; Gagné, Christian: DEAP: evolutionary algorithms made easy (2012)