moo: Genetic algorithm library
Moo library provides building blocks to build custom genetic algorithms in Haskell. They can be used to find solutions to optimization and search problems.
Variants supported out of the box: binary (using bit-strings) and continuous (real-coded). Potentially supported variants: permutation, tree, hybrid encodings (require customizations).
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.
[Skip to Readme]
|Versions [RSS]||1.0, 1.2|
|Dependencies||array, base (>=4 && <5), containers, gray-code (>=0.2.1), mersenne-random-pure64, MonadRandom, mtl (>=2), parallel (>=3.0), random (>=0.1), random-shuffle (>=0.0.2), time, vector [details]|
|Author||Sergey Astanin <firstname.lastname@example.org>|
|Maintainer||Sergey Astanin <email@example.com>|
|Category||AI, Algorithms, Optimisation, Optimization|
|Source repo||head: git clone git://github.com/astanin/moo.git|
|Uploaded||by SergeyAstanin at 2018-11-13T21:49:22Z|
|Reverse Dependencies||2 direct, 0 indirect [details]|
|Downloads||13589 total (33 in the last 30 days)|
|Rating||2.0 (votes: 1) [estimated by Bayesian average]|
|Status||Docs available [build log]
Last success reported on 2018-11-13 [all 1 reports]