simple-genetic-algorithm-mr VS Etage

Compare simple-genetic-algorithm-mr vs Etage and see what are their differences.

simple-genetic-algorithm-mr

Fork of simple-genetic-algorithm using MonadRandom (by JPMoresmau)

Etage

A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology. (by mitar)
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simple-genetic-algorithm-mr Etage
- -
2 0
- -
0.0 0.0
about 9 years ago over 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.
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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.

simple-genetic-algorithm-mr

Posts with mentions or reviews of simple-genetic-algorithm-mr. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning simple-genetic-algorithm-mr 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 simple-genetic-algorithm-mr and Etage you can also consider the following projects:

simple-genetic-algorithm - Simple parallel genetic algorithm implementation in pure Haskell

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

hnn - haskell neural network library

hasktorch - Tensors and neural networks in Haskell

simple-neural-networks - Simple parallel neural networks implementation in pure Haskell

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

liblinear-enumerator - Haskell bindings to liblinear

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.

hopfield - hopfield

neet - Neuroevolution of Augmented Topologies (NEAT) -- in Haskell

creatur - Framework for artificial life and other evolutionary algorithms.

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