moo
Genetic algorithm library http://www.github.com/astanin/moo/
Latest on Hackage:  1.2 
This package is not currently in any snapshots. If you're interested in using it, we recommend adding it to Stackage Nightly. Doing so will make builds more reliable, and allow stackage.org to host generated Haddocks.
Moo

< Moo. Breeding Genetic Algorithms with Haskell. >

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\ (oo)\_______
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Installation
Installation from Hackage
Hackage is a Haskell community’s package archive. This is where the latest versions of packages are published first. To install Moo from Hackage use CabalInstall:
 install Haskell Platform or GHC and CabalInstall
 run
cabal update
 run
cabal install moo
Installation with Stack
Stackage is a stable package archive. Stackage builds are supposed to
be reproducible. Stackage also provides Long Term Support releases.
To build Moo with Stackage dependencies, use the stack
tool:
 install
stack
 if necessary, install GHC: run
stack setup
 run:
stack update
 in the project source directory run:
stack build
 to run tests:
stack test
Build Status
Features
  Binary GA  Continuous GA 
++
Encoding  binary bitstring  sequence of real values 
  Gray bitstring  
++
Initialization  random uniform 
  constrained random uniform 
  arbitrary custom 
+
Objective  minimization and maximiation 
  optional scaling 
  optional ranking 
  optional niching (fitness sharing) 
+
Selection  roulette 
  stochastic universal sampling 
  tournament 
  optional elitism 
  optionally constrained 
  custom nonadaptive ^ 
+
Crossover  onepoint 
  twopoint 
  uniform 
  custom nonadaptive ^ 
 ++
   BLXα (blend) 
   SBX (simulated binary) 
   UNDX (unimodal normally 
   distributed) 
++
Mutation  point  Gaussian 
  asymmetric  
  constant frequency  
 ++
  custom nonadaptive ^ 
+
Replacement  generational with elitism 
  steady state 
+
Stop  number of generations 
condition  values of objective function 
  stall of objective function 
  custom or interactive (`loopIO`) 
  time limit (`loopIO`) 
  compound conditions (`And`, `Or`) 
+
Logging  pure periodic (any monoid) 
  periodic with `IO` 
+
Constrainted  constrained initialization 
optimization  constrained selection 
  death penalty 
+
Multiobjective  NSGAII 
optimization  constrained NSGAII 
^
nonadaptive: any function which doesn’t depend on generation number
There are other possible encodings which are possible to represent
with listlike genomes (type Genome a = [a]
):
 permutation encodings (
a
being an integer, or otherEnum
type)  tree encodings (
a
being a subtree type)  hybrid encodings (
a
being a sum type)
Contributing
There are many ways you can help developing the library:

I’m not a native speaker of English. If you are, please proofread and correct the comments and the documentation.

Moo is designed with possibility of implementing custom genetic operators in mind. If you write new operators (
SelectionOp
,CrossoverOp
,MutationOp
) or replacement strategies (StepGA
), consider contributing them to the library. In the comments please give a reference to an academic work which introduces or studies the method. Explain when or why it should be used. Provide tests and examples if possible. 
Implementing some methods (like adaptive genetic algorithms) will require to change some library types. Please discuss your approach first.

Contribute examples. Solutions of known problems with known optima and interesting properties. Try to avoid examples which are too contrived.
An example
Minimizing Beale’s function (optimal value f(3, 0.5) = 0):
import Moo.GeneticAlgorithm.Continuous
beale :: [Double] > Double
beale [x, y] = (1.5  x + x*y)**2 + (2.25  x + x*y*y)**2 + (2.625  x + x*y*y*y)**2
popsize = 101
elitesize = 1
tolerance = 1e6
selection = tournamentSelect Minimizing 2 (popsize  elitesize)
crossover = unimodalCrossoverRP
mutation = gaussianMutate 0.25 0.1
step = nextGeneration Minimizing beale selection elitesize crossover mutation
stop = IfObjective (\values > (minimum values) < tolerance)
initialize = getRandomGenomes popsize [(4.5, 4.5), (4.5, 4.5)]
main = do
population < runGA initialize (loop stop step)
print (head . bestFirst Minimizing $ population)
For more examples, see examples/ folder.