smarties
SimpleEA
| smarties | SimpleEA | |
|---|---|---|
| - | - | |
| 11 | 7 | |
| - | - | |
| 0.0 | 0.0 | |
| about 4 years ago | almost 8 years ago | |
| Haskell | Haskell | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
smarties
We haven't tracked posts mentioning smarties yet.
Tracking mentions began in Dec 2020.
SimpleEA
We haven't tracked posts mentioning SimpleEA yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
genprog - Genetic programming library
svm-simple - Simplified interface to bindings-svm
GA - Haskell module for working with genetic algorithms
hnn - haskell neural network library
HOpenCV - Haskell bindings for OpenCV
tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.
creatur - Framework for artificial life and other evolutionary algorithms.
keera-posture - Alleviate your back pain using Haskell and a webcam
cv-combinators - Functional Combinators for Computer Vision, currently using OpenCV as a backend
Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
moo - Genetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II.