attoparsec-arff VS HSGEP

Compare attoparsec-arff vs HSGEP and see what are their differences.

attoparsec-arff

An attoparsec-based parser for ARFF files in Haskell (by statusfailed)
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HSGEP

Haskell Gene Expression Programming Library (by mjsottile)
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attoparsec-arff HSGEP
- -
2 10
- -
0.0 0.0
over 11 years ago about 6 years ago
Haskell Mathematica
GNU General Public License v2.0 only 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.

attoparsec-arff

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

We haven't tracked posts mentioning attoparsec-arff 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 attoparsec-arff and HSGEP you can also consider the following projects:

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

hopfield - hopfield

heukarya - genetic programming in haskell

hnn - haskell neural network library

genprog - Genetic programming library

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

grenade - Deep Learning in Haskell

csp - Constraint satisfaction problem (CSP) solvers for 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.