The moo package
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, and tournament; with optional niching 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]
|Dependencies||array, base (==4.*), gray-code (>=0.2.1), mersenne-random-pure64, monad-mersenne-random, mtl (>=2), random (>=0.1), random-shuffle (>=0.0.2), time [details]|
|Author||Sergey Astanin <firstname.lastname@example.org>|
|Maintainer||Sergey Astanin <email@example.com>|
|Category||AI, Algorithms, Optimisation, Optimization|
|Source repository||head: git clone git://github.com/astanin/moo.git|
|Uploaded||Tue May 21 16:34:03 UTC 2013 by SergeyAstanin|
|Downloads||502 total (8 in the last 30 days)|
|Status||Docs uploaded by user
Build status unknown [no reports yet]
Hackage Matrix CI
For package maintainers and hackage trustees