hmep vs HSGEP

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hmep		HSGEP
	Project
-	Mentions	-
7	Stars	10
-	Growth	-
0.0	Activity	0.0
almost 2 years ago	Latest Commit	about 6 years ago
Haskell	Language	Mathematica
BSD 3-clause "New" or "Revised" License	License	BSD 3-clause "New" or "Revised" License

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.

HSGEP

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

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

What are some alternatives?

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

GA - Haskell module for working with genetic algorithms

creatur - Framework for artificial life and other evolutionary algorithms.

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

hopfield - hopfield

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.

genprog - Genetic programming library

Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.

grenade - Deep Learning in Haskell

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.