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Updates code to Swift 4.2
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@kelvinlauKL
kelvinlauKL authored Mar 6, 2019
1 parent de935b7 commit a1e2e9d
Showing 1 changed file with 84 additions and 108 deletions.
192 changes: 84 additions & 108 deletions Genetic/gen.playground/Contents.swift
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Original file line number Diff line number Diff line change
@@ -1,14 +1,12 @@
//: Playground - noun: a place where people can play

import Foundation

extension String {
var unicodeArray: [UInt8] {
return [UInt8](self.utf8)
}

var unicodeArray: [UInt8] {
return [UInt8](self.utf8)
}
}


let lex: [UInt8] = " !\"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~".unicodeArray

// This is the end goal and what we will be using to rate fitness. In the real world this will not exist
Expand All @@ -17,132 +15,110 @@ let OPTIMAL:[UInt8] = "Hello, World".unicodeArray
// The length of the string in our population. Organisms need to be similar
let DNA_SIZE = OPTIMAL.count

// size of each generation
// Size of each generation
let POP_SIZE = 50

// max number of generations, script will stop when it reach 5000 if the optimal value is not found
// Max number of generations, script will stop when it reaches 5000 if the optimal value is not found
let GENERATIONS = 5000

// The chance in which a random nucleotide can mutate (1/n)
let MUTATION_CHANCE = 100

func randomChar(from lexicon: [UInt8]) -> UInt8 {
let len = UInt32(lexicon.count-1)
let rand = Int(arc4random_uniform(len))
return lexicon[rand]
}

func randomPopulation(from lexicon: [UInt8], populationSize: Int, dnaSize: Int) -> [[UInt8]] {

var pop = [[UInt8]]()

(0..<populationSize).forEach { _ in
var dna = [UInt8]()
(0..<dnaSize).forEach { _ in
let char = randomChar(from: lexicon)
dna.append(char)
}
pop.append(dna)
var pop = [[UInt8]]()

(0..<populationSize).forEach { _ in
var dna = [UInt8]()
(0..<dnaSize).forEach { _ in
let char = lexicon.randomElement()!
dna.append(char)
}
return pop
pop.append(dna)
}
return pop
}

func calculateFitness(dna:[UInt8], optimal:[UInt8]) -> Int {
var fitness = 0
(0...dna.count-1).forEach { c in
fitness += abs(Int(dna[c]) - Int(optimal[c]))
}
return fitness
func calculateFitness(dna: [UInt8], optimal: [UInt8]) -> Int {
var fitness = 0
for index in dna.indices {
fitness += abs(Int(dna[index]) - Int(optimal[index]))
}
return fitness
}

func weightedChoice(items:[(dna:[UInt8], weight:Double)]) -> (dna:[UInt8], weight:Double) {

let total = items.reduce(0.0) { return $0 + $1.weight}

var n = Double(arc4random_uniform(UInt32(total * 1000000.0))) / 1000000.0

for item in items {
if n < item.weight {
return item
}
n = n - item.weight
func weightedChoice(items: [(dna: [UInt8], weight: Double)]) -> (dna: [UInt8], weight: Double) {

let total = items.reduce(0) { $0 + $1.weight }
var n = Double.random(in: 0..<(total * 1000000)) / 1000000.0

for item in items {
if n < item.weight {
return item
}
return items[1]
n = n - item.weight
}
return items[1]
}

func mutate(lexicon: [UInt8], dna:[UInt8], mutationChance:Int) -> [UInt8] {
var outputDna = dna

(0..<dna.count).forEach { i in
let rand = Int(arc4random_uniform(UInt32(mutationChance)))
if rand == 1 {
outputDna[i] = randomChar(from: lexicon)
}
func mutate(lexicon: [UInt8], dna: [UInt8], mutationChance: Int) -> [UInt8] {
var outputDna = dna
(0..<dna.count).forEach { i in
let rand = Int.random(in: 0..<mutationChance)
if rand == 1 {
outputDna[i] = lexicon.randomElement()!
}

return outputDna
}

return outputDna
}


func crossover(dna1:[UInt8], dna2:[UInt8], dnaSize:Int) -> [UInt8] {
let pos = Int(arc4random_uniform(UInt32(dnaSize-1)))

let dna1Index1 = dna1.index(dna1.startIndex, offsetBy: pos)
let dna2Index1 = dna2.index(dna2.startIndex, offsetBy: pos)

return [UInt8](dna1.prefix(upTo: dna1Index1) + dna2.suffix(from: dna2Index1))
func crossover(dna1: [UInt8], dna2: [UInt8], dnaSize: Int) -> [UInt8] {
let pos = Int.random(in: 0..<dnaSize)

let dna1Index1 = dna1.index(dna1.startIndex, offsetBy: pos)
let dna2Index1 = dna2.index(dna2.startIndex, offsetBy: pos)

return [UInt8](dna1.prefix(upTo: dna1Index1) + dna2.suffix(from: dna2Index1))
}

var population: [[UInt8]] = randomPopulation(from: lex, populationSize: POP_SIZE, dnaSize: DNA_SIZE)
var fittest = population[0]
import Foundation

func main() {
for generation in 0...GENERATIONS {
var weightedPopulation = [(dna: [UInt8], weight: Double)]()

for individual in population {
let fitnessValue = calculateFitness(dna: individual, optimal: OPTIMAL)
let pair = (individual, fitnessValue == 0 ? 1.0 : 1.0/Double(fitnessValue))
weightedPopulation.append(pair)
}

population = []

(0...POP_SIZE).forEach { _ in
let ind1 = weightedChoice(items: weightedPopulation)
let ind2 = weightedChoice(items: weightedPopulation)

let offspring = crossover(dna1: ind1.dna, dna2: ind2.dna, dnaSize: DNA_SIZE)
population.append(mutate(lexicon: lex, dna: offspring, mutationChance: MUTATION_CHANCE))
}

// generate the starting random population
var population:[[UInt8]] = randomPopulation(from: lex, populationSize: POP_SIZE, dnaSize: DNA_SIZE)
// print("population: \(population), dnaSize: \(DNA_SIZE) ")
var fittest = [UInt8]()
fittest = population[0]
var minFitness = calculateFitness(dna: fittest, optimal: OPTIMAL)

for generation in 0...GENERATIONS {

var weightedPopulation = [(dna:[UInt8], weight:Double)]()

// calulcated the fitness of each individual in the population
// and add it to the weight population (weighted = 1.0/fitness)
for individual in population {
let fitnessValue = calculateFitness(dna: individual, optimal: OPTIMAL)

let pair = ( individual, fitnessValue == 0 ? 1.0 : Double(100/POP_SIZE)/Double( fitnessValue ) )

weightedPopulation.append(pair)
}

population = []

// create a new generation using the individuals in the origional population
(0...POP_SIZE).forEach { _ in
let ind1 = weightedChoice(items: weightedPopulation)
let ind2 = weightedChoice(items: weightedPopulation)

let offspring = crossover(dna1: ind1.dna, dna2: ind2.dna, dnaSize: DNA_SIZE)

// append to the population and mutate
population.append(mutate(lexicon: lex, dna: offspring, mutationChance: MUTATION_CHANCE))
}

fittest = population[0]
var minFitness = calculateFitness(dna: fittest, optimal: OPTIMAL)

// parse the population for the fittest string
population.forEach { indv in
let indvFitness = calculateFitness(dna: indv, optimal: OPTIMAL)
if indvFitness < minFitness {
fittest = indv
minFitness = indvFitness
}
}
if minFitness == 0 { break; }
print("\(generation): \(String(bytes: fittest, encoding: .utf8)!)")

population.forEach { indv in
let indvFitness = calculateFitness(dna: indv, optimal: OPTIMAL)
if indvFitness < minFitness {
fittest = indv
minFitness = indvFitness
}
}
print("fittest string: \(String(bytes: fittest, encoding: .utf8)!)")

if minFitness == 0 { break }
print("\(generation): \(String(bytes: fittest, encoding: .utf8)!)")
}
print("fittest string: \(String(bytes: fittest, encoding: .utf8)!)")
}

main()

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