grenade VS HSGEP

Compare grenade vs HSGEP and see what are their differences.

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grenade HSGEP
5 -
1,440 10
- -
5.6 0.0
5 months ago about 6 years ago
Haskell Mathematica
BSD 2-clause "Simplified" 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.

grenade

Posts with mentions or reviews of grenade. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-23.

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 grenade and HSGEP you can also consider the following projects:

hasktorch - Tensors and neural networks in Haskell

creatur - Framework for artificial life and other evolutionary algorithms.

liblinear-enumerator - Haskell bindings to liblinear

hopfield - hopfield

simple-neural-networks - Simple parallel neural networks 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.

CV - Haskell wrappers and utilities for OpenCV machine vision library

genprog - Genetic programming library

nn - A tiny neural network 🧠

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.

hnn - haskell neural network library

GA - Haskell module for working with genetic algorithms