fei-examples VS HSGEP

Compare fei-examples vs HSGEP and see what are their differences.

HSGEP

Haskell Gene Expression Programming Library (by mjsottile)
AI
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fei-examples HSGEP
- -
1 10
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0.0 0.0
about 3 years ago about 6 years ago
Haskell Mathematica
BSD 3-clause "New" or "Revised" 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.

fei-examples

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

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

nn - A tiny neural network 🧠

creatur - Framework for artificial life and other evolutionary algorithms.

fei-base - Yet another wrapper of mxnet in Haskell

hopfield - hopfield

fei-nn - High level APIs for leaveraging neural networks with MXNet in Haskell

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

fei-cocoapi

genprog - Genetic programming library

fei-dataiter - Data Loading API of mxnet in Haskell

grenade - Deep Learning in Haskell

hasktorch - Tensors and neural networks 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.