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A simple binary genetic algorithm in Clojure, to demonstrate one way to write an evolutionary loop.
(ns evolvesum) ;; Lee Spector (lspector@hampshire.edu) 20111009
;; We evolve a vector of 100 zeros and ones that sums to a particular number.
;; An individual is a vector of 100 random bits.
(defn new-individual
[]
(vec (repeatedly 100 #(rand-int 2))))
;; An individual is mutated by possibly flipping a random bit.
(defn mutate
[individual]
(assoc individual (rand-int 100) (rand-int 2)))
;; The error of an individual is the difference between the sum of the bits
;; and the goal, which we hardcode here.
(defn error
[individual]
(Math/abs (- (reduce + individual) 73)))
;; An individual is better than another if it has lower error.
(defn better
[i1 i2]
(< (error i1) (error i2)))
;; We evolve a solution by starting with a random population and repeatedly
;; sorting, checking for a solution, and producing a new population.
;; We produce the new population by selecting and mutating the better half
;; of the current population.
(defn evolve
[popsize]
(loop [population (sort better (repeatedly popsize new-individual))]
(let [best (first population)]
(println "Best error:" (error best))
(if (zero? (error best))
(println "Success:" best)
(let [better-half (take (int (/ popsize 2)) population)]
(recur
(sort better (map mutate
(concat better-half better-half)))))))))
;; Run it with a population of 100:
(evolve 100)
;; Exercises:
;; - Create variables or parameters for the various hardcoded values.
;; - Avoid recomputing errors by making individuals pairs of [error genome].
;; - Print more information about the population each generation.
;; - Select parents via tournaments.
;; - Add crossover.
;; - Use a more interesting genome representation, for a more interesting
;; problem.
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