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Genetic Algorithms: Evolving “Hello World” in Javascript

I’ve always been extremely interested in evolution as an optimisation algorithm, this post is part of a series of write-ups on experiments using genetic techniques.

If you’re just interested in seeing the end result, check the demo here

Evolution is often described as being a “random” process. Some lay understandings of evolution imply that its process is not feasible given the number of “random” mutations required in order to produce a particular change or result in an organism. The key point neglected in these evaluations is that while mutations are a random component of evolution, the main power is its ability to accumulate “useful” mutations over time. The algorithm presented here is a simple illustration of the power of these kinds of algorithms, future articles with examine more advanced usage of the capabilities.

The problem

Generating the string “Hello, World!” (or any other) from random selections of letters. We start with an initial population of randomly generated strings and allow survivors whose string distance score is the smallest.

The problem space

Given the length of the string N, we will have to search through 26^n possibilities, leading to very rapid combinatorial explosion. For the “Hello, World!” string we will need to search through 2.4811529e+18 (that is, 2.48*10^18) different strings to find the one we’re looking for.

Strings are generated as candidates using the following function, resulting in each initial generation consisting of a series of randomly generated sequences of letters. Note that the alphabet doesn’t contain punctuation, even though it is used in the target string. This is because mutations will result in ASCII characters outside the specified range.

function random_string(len) {

  return new Array(len)
             .fill(0)
             .map((_) => ALPHABET.charAt(get_random_int(0,ALPHABET.length)))
             .join("")
  }

Elements of Genetic Algorithms

Since this example is very simple, it becomes easy to create a set of tunable parameters to investigate the effects on the resulting evolutionary process. These parameters are specified at the top of the JS file:

// string we're targetting
const TARGET_STR = "this string was produced by evolution";
// maximum length of initially created strings
const RANDOM_STR_MAX_LEN = 50;
// mutation rate. 40 means that on average each gene in 40 will be mutated 
const MUTATION_RATE = 40; 
// the tokens to draw from when creating initial strings
const ALPHABET = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" 
// the amount to increase or decrease each ASCII amount by during mutation
const MUTATION_STEPS = 2 
// penalty for being a different length from target string
const PENALTY_FACTOR = 100 
// initial generation size
const INITIAL_GENERATION_SIZE = 100;  
// number of candidates allowed to breed
const ALLOWED_TO_BREED = 40; 
// number of candidates allowed to survive
const ALLOWED_SURVIVORS = 40; 

Iteration

Each new generation is produced partially from the prior ones. Some of the best candidates will survive to the next round, and will also “breed” with other candidates to produce new candidates.

Fitness

After each round “fitness” should be evaluated. This is the digital equivalent of the survival/replication aspect of real evolution. A candidate’s fitness score is used in order to judge whether or not it will survive to the next generation. The algorithm described here uses a numerical distance factor created by summing the integer distance of the character codes of the candidate vs. the target string.

function fitness(str) {

  let smallest = Math.min.apply(null,[str.length, TARGET_STR.length]);
  let score = 0;
  score += Math.abs(str.length - TARGET_STR.length) * 100;

  for(let x=0;x<smallest;x++) {
    score += Math.abs(str.charCodeAt(x) - TARGET_STR.charCodeAt(x));
  }

  return score;

}

Mutation

Mutation introduces the ability to inroduce new genetic material in each round. Interesting results occur if the mutation function is removed or altered, and can result in a single dominant solution very early on with little or no new genetic material. For each child of the candidates in each generation, for every codon there is an 1 in MUTATION_RATE chance of a mutation occuring, which shifts a single letter’s character code by a random amount. If MUTATION_STEPS is 2, the random amount to shift will be between -1 and 1. If it’s 10, the random amount will be between -5 and 5.

function mutate(str) {

  return str
         .split("")
         .map((letter) => 
           (Math.round(Math.random()*MUTATION_RATE) == 2) 
           ? 
             String.fromCharCode(letter.charCodeAt(0)+Math.round(((Math.random()*MUTATION_STEPS)-(MUTATION_STEPS/2)))) 
             : 
             letter)
         .join("");

}

Breeding/Recombination

In order to retain the benefits of the optimal candidates, we cross-breed the best solutions. There are numerous ways to do this. One might be to simply take the odd numbered characters from one string, the even numbered characters from another string, and splice them together. Another could be to just use the first half of one string and the second half of the other. A solution often used in genetic algorithm research is creating “cut points” in the genomes of the two candidates. For instance, imagine two strings:

AAAAAAA
BBBBB
We select cut points 3 and 4 in string1, and 2 and 4 in string2, the resulting child ends up being:
  str1 0-3 AAA
  str2 2-4 BB
  str1 4-5 A
  = AAABBA

These functions are implemented as follows:

function breed(str1, str2) {

  let cp_1 = get_random_int(0, str1.length-1); 
  let cp_2 = get_random_int(cp_1, str1.length-1);
 
  // cp_1 is in the first string, defined as the first cut-point
  // this means we will take genetic material from str1 from 0 -> cp1
  // this will then be combined with the genetic material between cp_3 and cp_4 in str2
  // so we have str1[0,cp_1] + str2[cp_3,cp_4] + str1[cp_2,str1.size]

  let cp_3 = get_random_int(0, str2.length-1);
  let cp_4 = get_random_int(cp_3, str2.length-1);

  return (str1.slice(0, cp_1) + str2.slice(cp_3, cp_4) + str1.slice(cp_2, str2.length)).toString();

}

Put it all together and run it in the console and you end up with a generational history something like this:

 [lWTogtaMKlaXTHFozonCcqkpIrpESsEIqSSl, 916]
 [hduqCqurauqCqtrEsvrhprahDawErvrpvrpen, 349]
 [ieprBstrhtrDrtrEsvqbtaagDbwEavrputagn, 295]
 [hepsDrurirqCrdsCprrctaafBbxDavpptughn, 256]
 [iegsCquritgCrctBprpct#beBbxDcuonuthgn, 232]
 [uggtAqtrithBvbtBprpbtcbdBbxDcuonuthgn, 210]
 [uggs#rtrjnhAwasBprqctcid#ayCcvolurihn, 193]
 [ughs#rtrinh#xatAprpducdd#ayBdvnlutign, 180]
 [thhs#strinh#wat#prpcucdd#bxBdvnlutiin, 169]
 [this#strinh#wat#qroduced#byBdvnlutiin, 160]
 [uhis#strinh#wat#pqoduced#by#cvolutiin, 152]
 [this#string#xas8pqpduced#by#evoluthjn, 142]
 [this#string9was6qqoduced9by#evoluuijn, 134]
 [thhs9string7was8produced6by#evolutikn, 125]
 [this7strhng7was8prodtced5by#evolutiln, 122]
 [this7rtrgng5was2produced3bz#evolutimn, 113]
 [thhs6strgng3was2producfd2by8evolution, 105]
 [this6strgng1was2produced2by5evolution, 98]
 [thjs4string0was1producee0by3euolution, 91]
 [thjs3string0was1produced0by1fvolution, 87]
 [this1ssring#was#prodtced#cy#evplutjon, 80]
 [thit0string#was#prodtced#bz#evolution, 75]
 [this0string#was#prodtced#by#euolutjon, 69]
 [this#string#was#produced#by#evoluuion, 63]
 [this#ssring#was#produced#by#evolution, 59]
 [this#string#was#qroduced#by#fvolution, 55]
 [this#string#was#prodvced#by#evolution, 51]
 [this#string#was#psoduced#by#evolution, 46]
 [this#string#was#producdd#by#evolutipn, 38]
 [this#string#was#procuced#by#evplutioo, 26]
 [uhis#string#was#produced#by#evolutioo, 22]
 [this#stoing#was#produced#by evolution, 20]
 [shis#stting#was#produced#by evolution, 17]
 [tiis stting#was#produced bx evnlutiom, 11]
 [this#stting was#procuced bx evnlution, 9]
 [this#stsing was producee by evomution, 4]
 [this string wat producee by evolution, 2]

Feel free to try the live version here or check the code out from github to play with.

Published ingenetic algorithmsjavascript

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