A genome generation and evolution library.
Last updated 2 years ago by fiberwire .
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A Genome generation and evolution library

Note: This library is still in the early stages of development, and should not be considered producion-ready.

This library is written in TypeScript, and I recommend using it with a TypeScript project.

What is enome?

enome is a javascript/typescript library that allows you to asynchronously (using rxjs) evolve any kind of object you can think of.

enome has three main parts to its evolution system.

  • Organism:

    • an Organism is just an object that contains a genotype and a phenotype and has the ability to interact with an Environment.
      • a genotype, in this case, is a Genome, which contains genetic information that you use to create a phenotype.
      • a phenotype, in this case, is whatever kind of object you would like to evolve.
    • Organisms record data as they are interacting with the Environment.
      • Once the Organism has done a specified number of interactions, it evaluates itself based on the data it collected and sends the evaluation to the Population, which will decide how to evolve the organism.
      • You specify the function that determines the fitness of the Organism based on its data.
  • Environment:

    • an Environment is essentially just an asynchronous state container.
    • You interact with an Environment by sending IStateUpdates to its state property.
    • an Environment may have multiple Organisms interacting with it at a time.
    • Environments have an interactionRate property which you can set that limits how often it accepts IStateUpdates (think of it like a frame rate).
      • Environments deal with multiple asynchronous sources of incoming IStateUpdates by buffering them over time based on the interactionRate and then randomly choosing between them. The Environment will only accept state updates that are based on the current state, otherwise state updates could happen out of order (think of it like an asynchronous way of having a shared order of events even though everything is happening out of order, technically).
  • Population

    • Populations populate environments with organisms.
    • Populations are also in charge of determining how Organisms evolve.
      • when a Population receives an evaluation, it looks at the fitness and determines what it wants to do with the genotype.
        • Populations can update the genotype in the following ways:
          • Reproduce will mix the genomes of the top organisms in the population based on fitness. (You can specify the percentage of organisms that qualifies as "top", which lets you determine whether you want to refine what is already working well or find new solutions)
          • Mutate will give each value in the Genome's sequence a chance to mutate and has two different mutation methods to choose from.
            • sub will substitute the value for a new randomly generated one.
            • avg will average the value with a new randomly generated one.
          • Randomize will replace the genome with a new randomly generated one (using the same options).
          • Keep will just send the organism back into the environment. This is good for when you get a genotype with a very good fitness and you don't want it to be mutated which has the possibility of making it worse.
        • By default, Populations will choose between the different update methods randomly based on an array of weights that you provide.

Underlying the evolution system are Genomes and Genes:

  • Genome:

    • A Genome is just a container for genetic information, or a sequence.
    • At its heart, a sequence is just an array of numbers between 0 and 1.
    • When created, the Genome takes that sequence and produces Genes from it.
    • Genome provides the g property which allows you to get the next Gene in the list, so you can consume them one by one in a queue-like manner.
  • Gene:

    • A Gene is just a container for a value between 0 and 1.
    • Genes give you methods that interpolate their value into a value that it useful when creating the phenotype.
      • for instance, say your phenotype is a first and last name:
      interface Name {
          first: string;
          last: string;
      interface NameOptions extends IGenomeOptions {
          firstMinLength: number;
          firstMaxLength: number;
          lastMinLength: number;
          lastMaxLength: number;
      public createPhenotype(genome: Genome<NameOptions>): Name {
          // determine length of first name
          const firstLength = genome.g.int(
          // determine length of last name
          const lastLength = genome.g.int(
          //create first name
          const first = _.range(firstLength)
              .map(i => genome.g.letter())
              .reduce((first, letter) => `${first}${letter}`)
          // create last name
          const last = _.range(lastLength)
              .map(i => genome.g.letter())
              .reduce((last, letter) => `${last}${letter}`)
          return { first, last };

How enome generates Genomes:

  • Generates a sequence of values between 0 and 1.
  • Groups those values into Genes by averaging them together
    • This results in the Genome being less sensitive to mutation
    • The sensitivity is customizable by varying the number of values that go into each Gene
  • Groups those Genes into a Genome
    • Genome exposes a property called g that allows you to get the next Gene in the Genome.
      • This allows you to pass the Genome around, consuming its Genes as you need them.

Install instructions

npm install enome

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