DEAP: evolutionary algorithms made easy

By Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné. 2012

2,138 citations recorded by the source corpus.

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Citations in the corpus

Local Rule-Based Explanations of Black Box Decision Systems (2018, methodology): “LORE has been developed in Python 9 , using, for the genetic neighborhood generation, the deap library [13], and for decision tree induction (the interpretable predictor), the yadt system [32], which is a C4.5 implementation with multi-way splits of categorical attributes.”
Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, methodology): “To automatically generate these tree-based pipelines, we used a well-known evolutionary computation technique called genetic programming (GP) as implemented in the Python package DEAP [13].”
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning (2016, methodology): “To automatically generate and optimize these tree-based pipelines, we use a genetic programming (GP) algorithm [1] as implemented in the Python package DEAP [7].”
Pymoo: Multi-Objective Optimization in Python (2020, methodology): “A Distributed Evolutionary Algorithms in Python (DEAP) [16] is novel evolutionary computation framework for rapid prototyping and testing of ideas.”
A Distributional View on Multi-Objective Policy Optimization (2020, unknown): “We use DEAP (Fortin et al., 2012) to compute hypervolumes.”
Discovering Symbolic Models from Deep Learning with Inductive Biases (2020, unknown): “Other community packages such as [26, 27, 28, 29, 30, 31, 32, 33, 34], could likely also be used and achieve similar results (although [30] appears to not fit constants in equations, so could not be used here).”
Pattern Recognition (2015, unknown): “We use the shared weights network – Deeplab-VGG16 to learn spatial and temporal deep features and fuse them in multiscale in fusion blocks to form spatio-temporal feature maps.”
Sapienz: multi-objective automated testing for Android applications (2016, methodology): “We have implemented the Sapienz tool on top of the Deap framework [31] for multi-objective test suite evolution.”
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization (2021, background): “In addition, evolutionary algorithms are also known as eﬃcient black-box optimizers (Fortin et al., 2012; Olson et al., 2016; Loshchilov and Hutter, 2016; Rapin and Teytaud, 2018; Awad et al., 2021).”
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science (2016, methodology): “To automatically generate and optimize these tree-based pipelines, we use a well-known evolutionary computation technique called genetic programming (GP) as implemented in the Python package DEAP [6].”
Easy Hyperparameter Search Using Optunity (2014, unknown): “Optunity has a single dependency on DEAP (Fortin et al., 2012) for the CMA-ES solver.”
PyGAD: an intuitive genetic algorithm Python library (2021, methodology): “A comparison between DEAP and Pyevolve shows that the number of code lines needed to solve the OneMax problem is 59 for DEAP and 378 for Pyevolve [3].”
ALLMAPS: robust scaffold ordering based on multiple maps (2015, methodology): “For crossover operator, we use the ‘ Partially Mapped Crossover ’ (PMX) function that was shown to speed up convergence [40].”
Recycling Privileged Learning and Distribution Matching for Fairness (2017, methodology): “We use the DEAP toolbox [42] for experimenting with EMO.”
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice (2020, methodology): “DEAP [121] is a novel evolutionary computation package for Python that contains several evolutionary algorithms like GA and PSO.”