Safe Haskell | None |
---|---|

Language | Haskell2010 |

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 (SinInt xs) (SinInt ys) = return [ SinInt (L.zipWith (\x y -> (x+y)/2) xs ys) ] mutation (SinInt xs) = do idx <- getRandomR (0, length xs - 1) dx <- getRandomR (-10.0, 10.0) let t = xs !! idx xs' = take idx xs ++ [t + t*dx] ++ drop (idx+1) xs return $ SinInt xs' fitness int = let max_err = 1000.0 in max_err - (min (err int) max_err) randomSinInt gen = lst <- replicateM polynomialOrder (getRandomR (-10.0,10.0)) 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

- class NFData a => Chromosome a where
- runGA :: (RandomGen g, Chromosome a) => g -> Int -> Double -> Rand g a -> (a -> Int -> Bool) -> a
- runGAIO :: Chromosome a => Int -> Double -> RandT StdGen IO a -> (a -> Int -> IO Bool) -> IO a
- zeroGeneration :: (Monad m, RandomGen g, Chromosome a) => RandT g m a -> Int -> RandT g m [a]
- nextGeneration :: (Monad m, RandomGen g, Chromosome a) => [a] -> Int -> Double -> RandT g m [a]

# Documentation

class NFData a => Chromosome a where Source

Chromosome interface

:: (RandomGen g, Chromosome a) | |

=> g | Random number generator |

-> Int | Population size |

-> Double | Mutation probability [0, 1] |

-> Rand g a | Random chromosome generator (hint: use currying or closures) |

-> (a -> Int -> Bool) | Stopping criteria, 1st arg - best chromosome, 2nd arg - generation number |

-> a | Best chromosome |

Pure GA implementation.

:: Chromosome a | |

=> Int | Population size |

-> Double | Mutation probability [0, 1] |

-> RandT StdGen IO a | Random chromosome generator (hint: use currying or closures) |

-> (a -> Int -> IO Bool) | Stopping criteria, 1st arg - best chromosome, 2nd arg - generation number |

-> IO a | Best chromosome |

Non-pure GA implementation.

:: (Monad m, RandomGen g, Chromosome a) | |

=> RandT g m a | Random chromosome generator (hint: use closures) |

-> Int | Population size |

-> RandT g m [a] | Zero generation |

Generate zero generation. Use this function only if you are going to implement your own runGA.

:: (Monad m, RandomGen g, Chromosome a) | |

=> [a] | Current generation |

-> Int | Population size |

-> Double | Mutation probability |

-> RandT g m [a] | Next generation ordered by fitness (best - first) |

Generate next generation (in parallel) using mutation and crossover. Use this function only if you are going to implement your own runGA.