hmep VS Etage

Compare hmep vs Etage and see what are their differences.

hmep

Haskell Multi Expression Programming implemented with the focus on speed (by masterdezign)

Etage

A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology. (by mitar)
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hmep Etage
- -
7 0
- -
0.0 0.0
almost 2 years ago almost 10 years ago
Haskell Haskell
BSD 3-clause "New" or "Revised" License GNU Lesser General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

hmep

Posts with mentions or reviews of hmep. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning hmep yet.
Tracking mentions began in Dec 2020.

Etage

Posts with mentions or reviews of Etage. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Etage yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing hmep and Etage you can also consider the following projects:

GA - Haskell module for working with genetic algorithms

keera-posture - Alleviate your back pain using Haskell and a webcam

hasktorch - Tensors and neural networks in Haskell

hnn - haskell neural network library

tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

HSGEP - Haskell Gene Expression Programming Library

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.

creatur - Framework for artificial life and other evolutionary algorithms.

simple-genetic-algorithm-mr - Fork of simple-genetic-algorithm using MonadRandom