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.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Salman, Sinan; Alaswad, Suzan: Alleviating road network congestion: traffic pattern optimization using Markov chain traffic assignment (2018)
- Krawiec, Krzysztof; Liskowski, Paweł: Adaptive test selection for factorization-based surrogate fitness in genetic programming (2017)
- Sun, Jun; Wu, Xiaojun; Palade, Vasile; Fang, Wei; Shi, Yuhui: Random drift particle swarm optimization algorithm: convergence analysis and parameter selection (2015)
- Lara-Cabrera, Raúl; Cotta, Carlos; Fernández-Leiva, Antonio: On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms (2014) ioport
- 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
- Fortin, Félix-Antoine; De Rainville, François-Michel; Gardner, Marc-André; Parizeau, Marc; Gagné, Christian: DEAP: evolutionary algorithms made easy (2012)