simple-genetic-algorithm VS CarneadesDSL

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

simple-genetic-algorithm

Simple parallel genetic algorithm implementation in pure Haskell (by afiskon)

CarneadesDSL

An implementation and DSL for the Carneades argumentation model. (by nebasuke)
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simple-genetic-algorithm CarneadesDSL
- -
12 2
- -
0.0 0.0
over 6 years ago about 10 years ago
Haskell Haskell
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.
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simple-genetic-algorithm

Posts with mentions or reviews of simple-genetic-algorithm. 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 yet.
Tracking mentions began in Dec 2020.

CarneadesDSL

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

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

What are some alternatives?

When comparing simple-genetic-algorithm and CarneadesDSL you can also consider the following projects:

simple-genetic-algorithm-mr - Fork of simple-genetic-algorithm using MonadRandom

hnn - haskell neural network library

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.

csp - Constraint satisfaction problem (CSP) solvers for Haskell

GA - Haskell module for working with genetic algorithms

heukarya - genetic programming in haskell

cv-combinators - Functional Combinators for Computer Vision, currently using OpenCV as a backend

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

svm-simple - Simplified interface to bindings-svm

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

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