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ardiad
DEoptim:Global Optimization by Differential Evolution
Implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.
Maintained by Katharine Mullen. Last updated 2 years ago.
differential-evolutionevolutionary-algorithmglobal-optimizationoptimization
29 stars 11.49 score 680 scripts 125 dependents
facebookexperimental
Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science
Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Maintained by Gufeng Zhou. Last updated 15 days ago.
adstockingbudget-allocationcost-response-curveeconometricsevolutionary-algorithmgradient-based-optimisationhyperparameter-optimizationmarketing-mix-modelingmarketing-mix-modellingmarketing-sciencemmmridge-regression
1.3k stars 10.27 score 95 scripts
jakobbossek
ecr:Evolutionary Computation in R
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Maintained by Jakob Bossek. Last updated 2 years ago.
combinatorial-optimizationevolutionary-algorithmevolutionary-algorithmsevolutionary-strategygenetic-algorithm-frameworkmetaheuristicsmulti-objective-optimizationoptimizationoptimization-frameworkcpp
43 stars 7.36 score 89 scripts 2 dependents