mca*_*dre 7 memory stack-overflow random haskell genetic-algorithm
我正在写一个遗传算法来生成字符串"helloworld".但是当n为10,000或更多时,evolve函数会创建堆栈溢出.
module Genetics where
import Data.List (sortBy)
import Random (randomRIO)
import Control.Monad (foldM)
class Gene g where
-- How ideal is the gene from 0.0 to 1.0?
fitness :: g -> Float
-- How does a gene mutate?
mutate :: g -> IO g
-- How many species will be explored?
species :: [g] -> Int
orderFitness :: (Gene g) => [g] -> [g]
orderFitness = reverse . sortBy (\a b -> compare (fitness a) (fitness b))
compete :: (Gene g) => [g] -> IO [g]
compete pool = do
let s = species pool
variants <- (mapM (mapM mutate) . map (replicate s)) pool
let pool' = (map head . map orderFitness) variants
return pool'
evolve :: (Gene g) => Int -> [g] -> IO [g]
evolve 0 pool = return pool
evolve n pool = do
pool' <- compete pool
evolve (n - 1) pool'
Run Code Online (Sandbox Code Playgroud)
随着species pool = 8,8个基因库重复到8组.每组变异,并选择每组的最适合进一步进化(回到8个基因).
感谢唐的deepseq建议,我能够将问题范围缩小到mapM mutate导致太多重击的问题。新版本有mutate',用于seq防止 thunking。
module Genetics where
import Data.List (maximumBy)
import Random (randomRIO)
class Gene g where
-- How ideal is the gene from 0.0 to 1.0?
fitness :: g -> Float
-- How does a gene mutate?
mutate :: g -> IO g
-- How many species will be explored in each round?
species :: [g] -> Int
best :: (Gene g) => [g] -> g
best = maximumBy (\a b -> compare (fitness a) (fitness b))
-- Prevents stack overflow
mutate' :: (Gene g) => g -> IO g
mutate' gene = do
gene' <- mutate gene
gene' `seq` return gene'
drift :: (Gene g) => [[g]] -> IO [[g]]
drift = mapM (mapM mutate')
compete :: (Gene g) => [g] -> IO [g]
compete pool = do
let islands = map (replicate (species pool)) pool
islands' <- drift islands
let representatives = map best islands'
return representatives
evolve :: (Gene g) => Int -> [g] -> IO [g]
evolve 0 pool = return pool
evolve n pool = compete pool >>= evolve (n - 1)
Run Code Online (Sandbox Code Playgroud)