genprog alternatives and similar packages
Based on the "AI" category.
Alternatively, view genprog alternatives based on common mentions on social networks and blogs.
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tensor-safe
A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. -
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. -
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simple-genetic-algorithm
Simple parallel genetic algorithm implementation in pure 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 -
HaVSA
HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau. -
hmep
Haskell Multi Expression Programming implemented with the focus on speed -
fei-nn
High level APIs for leaveraging neural networks with MXNet in Haskell -
Etage
A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology. -
CarneadesDSL
An implementation and DSL for the Carneades argumentation model. -
simple-genetic-algorithm-mr
Fork of simple-genetic-algorithm using MonadRandom -
attoparsec-arff
An attoparsec-based parser for ARFF files in Haskell
Updating dependencies is time-consuming.
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They vary from L1 to L5 with "L5" being the highest.
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README
genprog: Genetic Programming Library
This package provides a genetic programming framework. Genetic programming is an evolutionary technique, inspired by biological evolution, to evolve programs for solving specific problems. A genetic program is represented by a value of an algebraic datatype and associated with a custom-defined fitness value indicating the quality of the solution. Starting from a randomly generated initial population of genetic programs, the genetic operators of selection, crossover, and mutation are used to evolve programs of increasingly better quality.
Check out the documentation and examples on Hackage.