moo: Genetic algorithm library
|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 <email@example.com>|
|Maintainer||Sergey Astanin <firstname.lastname@example.org>|
|Category||AI, Algorithms, Optimisation, Optimization|
|Source repo||head: git clone git://github.com/astanin/moo.git|
|Uploaded||by SergeyAstanin at Tue May 21 16:34:03 UTC 2013|
|Downloads||656 total (6 in the last 30 days)|
|Rating||2.0 (votes: 1) [estimated by rule of succession]|
|Status||Docs uploaded by user
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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.
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