genetic-nodejs-multithread v0.3.7
Genetic-multithread.js
Advanced genetic and evolutionary algorithm library written in Javascript by Sub Protocol.
Changes from original version
This is a modified genetic repo optimized for nodejs with multi thread performance optimization. it uses experimental feature worker_threads This version only supports genetic with node js, doesn't support running in browser.
requires to run program with node --experimental-worker
Examples
example in nodejs node --experimental-worker examples/nodeJsFitting.js
Install
npm install genetic-nodejs-multithreadPopulation Functions
The genetic-js interface exposes a few simple concepts and primitives, you just fill in the details/features you want to use.
| Function | Return Type | Required | Description |
|---|---|---|---|
| seed() | Individual | Yes | Called to create an individual, can be of any type (int, float, string, array, object) |
| fitness(individual) | Float | Yes | Computes a fitness score for an individual |
| mutate(individual) | Individual | Optional | Called when an individual has been selected for mutation |
| crossover(mother, father) | Son, Daughter | Optional | Called when two individuals are selected for mating. Two children should always returned |
| optimize(fitness, fitness) | Boolean | Yes | Determines if the first fitness score is better than the second. See Optimizer section below |
| select1(population) | Individual | Yes | See Selection section below |
| select2(population) | Individual | Optional | Selects a pair of individuals from a population. Selection |
| generation(pop, gen, stats) | Boolean | Optional | Called for each generation. Return false to terminate end algorithm (ie- if goal state is reached) |
| notification(pop, gen, stats, isFinished) | Void | Optional | Runs in the calling context. All functions other than this one are run in a web worker. |
Optimizer
The optimizer specifies how to rank individuals against each other based on an arbitrary fitness score. For example, minimizing the sum of squared error for a regression curve Genetic.Optimize.Minimize would be used, as a smaller fitness score is indicative of better fit.
| Optimizer | Description |
|---|---|
| Genetic.Optimize.Minimizer | The smaller fitness score of two individuals is best |
| Genetic.Optimize.Maximizer | The greater fitness score of two individuals is best |
Selection
An algorithm can be either genetic or evolutionary depending on which selection operations are used. An algorithm is evolutionary if it only uses a Single (select1) operator. If both Single and Pair-wise operations are used (and if crossover is implemented) it is genetic.
| Select Type | Required | Description |
|---|---|---|
| select1 (Single) | Yes | Selects a single individual for survival from a population |
| select2 (Pair-wise) | Optional | Selects two individuals from a population for mating/crossover |
Selection Operators
| Single Selectors | Description |
|---|---|
| Genetic.Select1.Tournament2 | Fittest of two random individuals |
| Genetic.Select1.Tournament3 | Fittest of three random individuals |
| Genetic.Select1.Fittest | Always selects the Fittest individual |
| Genetic.Select1.Random | Randomly selects an individual |
| Genetic.Select1.RandomLinearRank | Select random individual where probability is a linear function of rank |
| Genetic.Select1.Sequential | Sequentially selects an individual |
| Pair-wise Selectors | Description |
|---|---|
| Genetic.Select2.Tournament2 | Pairs two individuals, each the best from a random pair |
| Genetic.Select2.Tournament3 | Pairs two individuals, each the best from a random triplett |
| Genetic.Select2.Random | Randomly pairs two individuals |
| Genetic.Select2.RandomLinearRank | Pairs two individuals, each randomly selected from a linear rank |
| Genetic.Select2.Sequential | Selects adjacent pairs |
| Genetic.Select2.FittestRandom | Pairs the most fit individual with random individuals |
var genetic = Genetic.create();
// more likely allows the most fit individuals to survive between generations
genetic.select1 = Genetic.Select1.RandomLinearRank;
// always mates the most fit individual with random individuals
genetic.select2 = Genetic.Select2.FittestRandom;
// ...Configuration Parameters
| Parameter | Default | Range/Type | Description |
|---|---|---|---|
| size | 250 | Real Number | Population size |
| crossover | 0.9 | 0.0, 1.0 | Probability of crossover |
| mutation | 0.2 | 0.0, 1.0 | Probability of mutation |
| iterations | 100 | Real Number | Maximum number of iterations before finishing |
| fittestAlwaysSurvives | true | Boolean | Prevents losing the best fit between generations |
| skip | 0 | Real Number | Setting this higher throttles back how frequently genetic.notification gets called in the main thread. |
| workerPath | '' | String | NodeJS only, set a custom fitness worker path |
| workersCount | 0 | number | NodeJS only, set how many multi thread workers to use, set 0 to disable multi threading |
Building
To clone, build, and test Genetic.js issue the following command:
git clone git@github.com:subprotocol/genetic-js.git && make distcheck| Command | Description |
|---|---|
| make | Automatically install dev-dependencies |
Contributing
Feel free to open issues and send pull-requests.