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**Monthly Downloads**: 9

**Programming language**: Haskell

**License**: BSD 3-clause "New" or "Revised" License

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## README

## simple-genetic-algorithm-mr

This is a simple fork of simple-genetic-algorithm, using MonadRandom instead of manually threading the random generator, for convenience.

The readme for simple-genetic-algorithm is below:

## simple-genetic-algorithm

Simple parallel genetic algorithm implementation in pure Haskell

Sin() function interpolation example ( src/MainSin.hs ):

```
import AI.GeneticAlgorithm.Simple
import System.Random
import Text.Printf
import Data.List as L
import Control.DeepSeq
newtype SinInt = SinInt [Double]
instance NFData SinInt where
rnf (SinInt xs) = rnf xs `seq` ()
instance Show SinInt where
show (SinInt []) = "<empty SinInt>"
show (SinInt (x:xs)) =
let start = printf "%.5f" x
lst = [1 :: Int ..]
end = concat $ zipWith (\c p -> printf "%+.5f" c ++ "X^" ++ show p) xs lst
in start ++ end
polynomialOrder = 4 :: Int
err :: SinInt -> Double
err (SinInt xs) =
let f x = snd $ L.foldl' (\(mlt,s) coeff -> (mlt*x, s + coeff*mlt)) (1,0) xs
in maximum [ abs $ sin x - f x | x <- [0.0,0.001 .. pi/2]]
instance Chromosome SinInt where
crossover g (SinInt xs) (SinInt ys) =
( [ SinInt (L.zipWith (\x y -> (x+y)/2) xs ys) ], g)
mutation g (SinInt xs) =
let (idx, g') = randomR (0, length xs - 1) g
(dx, g'') = randomR (-10.0, 10.0) g'
t = xs !! idx
xs' = take idx xs ++ [t + t*dx] ++ drop (idx+1) xs
in (SinInt xs', g'')
fitness int =
let max_err = 1000.0 in
max_err - (min (err int) max_err)
randomSinInt gen =
let (lst, gen') =
L.foldl'
(\(xs, g) _ -> let (x, g') = randomR (-10.0,10.0) g in (x:xs,g') )
([], gen) [0..polynomialOrder]
in (SinInt lst, gen')
stopf :: SinInt -> Int -> IO Bool
stopf best gnum = do
let e = err best
_ <- printf "Generation: %02d, Error: %.8f\n" gnum e
return $ e < 0.0002 || gnum > 20
main = do
int <- runGAIO 64 0.1 randomSinInt stopf
putStrLn ""
putStrLn $ "Result: " ++ show int
```

For more details see: