Simple parallel genetic algorithm implementation.

 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
             end = concat $ zipWith (\c p -> printf "%+.5f" c ++ "X^" ++ show p) xs [1 :: Int ..]
         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