moo-1.2.0.0.0.0.1

Synopsis

Genetic algorithm library

Description

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.

Author

Sergey Astanin <s.astanin@gmail.com>

Maintainer

Sergey Astanin <s.astanin@gmail.com>

License

BSD3

Source

https://github.com/input-output-hk/moo
Commit hash: 8c487714fbfdea66188fcb85053e7e292e0cc348

Timestamp

2022-10-19T10:03:21Z

Revisions

None

Dependencies

library moo:

["base >=4 && <5","MonadRandom","mersenne-random-pure64","gray-code >=0.2.1","random >=0.1","random-shuffle >=0.0.2","mtl >=2","time","array","parallel >=3.0","vector","containers"]

test-suite moo-tests:

["moo","base <5","HUnit","random >=0.1","random-shuffle >=0.0.2","MonadRandom","mersenne-random-pure64","gray-code >=0.2.1","mtl","time","array","containers","parallel >=3.0","vector","containers"]