Genkit

A library for implementing genetic algorithm based applications.

Installation

Install genkit using npm package manager

npm install --save genkit

API Guide

Config object

Create a Config object, which stores the context for rest of the operations.

import * as gk from 'genkit';

let gaconfig: gk.Config = {
            dnaCodes: dnaCodes,
            evalType: evalType,
            evalFn: evalFn,
            mutate: mutateFn,
            mate: mateFn
};

Config is defined as

type Config = {
    dnaCodes: Array<any>;
    evalType: EvalType;
    evalFn: Function;
    mutate: Function;
    mate: Function;
};

where

Parameter	Details
dnaCodes	Array of dna codes of any type, depending on what you are trying to model. Usually an Array<string> would be sufficient. For example, if you are trying to model the human genome, the dnaCode would be ['A', 'T', 'G', 'C']. All dnas will be a sequence of elements of this array.
evalType	One of EvalType.ET_COST, EvalType.ET_FITNESS
evalFn	The evaluation function that takes a dna (sequence of dna codes) and returns either the cost or fitness of it depending on the evalType. It must be coupled with the above specified evalType. The type signature is (str: Array<any>): number
mutate	A function that takes a dna code (any) and gives out another dna code (any) by selecting from a given array of dna codes based on some logic. The type signature is (dnaCode: any, dnaCodes: Array<any>): any.
mate	A function that takes two dna sequences (parents) and generates an output dna sequence (child). The type signature is: (str: Array, str2: Array): Array`

Generate initial population (random)

Once you have a Config object, you can start with creating a random population of a given dna sequence length and population count using the function randomPopulation as follows:

let p = gk.randomPopulation(gaconfig, 10, 50);

It returns a Population object, which is defined as follows:

type Population = {
    elements: Array<Gene>;
    generation: number;
};

It contains an array of Gene objects, which represents the dna sequence of each member of the population. The Gene object is defined as:

type Gene = {
    code: Array<any>;
    score: number;
    fitness: number;
    cost: number;
};

Evolve population

The next step would be to iteratively evolve the initial population towards more and more fitness or less and less cost at each step (depending on what you provided in the evalType parameter while creating the Config).

To calculate the score of any Population object, use the score function, which will return a new Population object that has its members scored according to the evalType provided in the Config object.

p_scored = gk.score(gaconfig, p)

To get to the next step in evolution, call the evolve function on a scored Population object as follows:

p_next = gk.evolve(gaconfig, p_scored, 0.01);

The last parameter in evolve function is the mutation rate. It is the probability of mutation happening during each mating operation. The higher the mutation rate, the more frequently the population can be expected to get random dna sequences which are not just derived from parents. Usually, low mutation rates work well because they provide enough opportunity for a given member to participate in natural selection.

You should apply some convergence checks after each step in evolution to find out it the current Population object is good enough for your purpose. If so, end the iterations. For more convinience, each Gene object also has a score member, which is a number betwee 0 and 1, which represents a normalized "goodness" of that Gene object. So a convergence check can be simple done on the value of the score.

// Using underscore.js
let best_gene = _.max(p_scored.elements, (e) => e.score);

That's it. For a working sample application, refer to this project: https://github.com/vaibhav276/genkit-examples/tree/master/target-text. (Most relevant code is in https://github.com/vaibhav276/genkit-examples/blob/master/target-text/src/app/ga-runner.service.ts)