# genetic-nodejs-multithread > Advanced genetic and evolutionary algorithm library Latest version **0.3.7** (published 2020-01-12) · BSD license · 0 weekly downloads ## Install ```sh npm install genetic-nodejs-multithread pnpm add genetic-nodejs-multithread yarn add genetic-nodejs-multithread bun add genetic-nodejs-multithread ``` ## Health **Score 15/100 (F)** — status: abandoned. Positive: no vulnerabilities. Warnings: low downloads; no types; no esm support; pre 1.0. Negative: abandoned; low maintenance score. ## Facts | | | |---|---| | Version | 0.3.7 | | Published | 2020-01-12 | | First published | 2019-04-11 | | Weekly downloads | 0 | | License | BSD | | TypeScript types | none | | Module format | CommonJS | | Dependencies | 0 | | Unpacked size | 35.1 KB | | Known vulnerabilities | 0 | | Install scripts | no | | Author | subprotocol | | Maintainers | whateverhow | | Keywords | genetic, evolution, algorithm, nodejs | ## Links - npm: https://www.npmjs.com/package/genetic-nodejs-multithread - Repository: https://github.com/z-hao-wang/genetic-js - Homepage: https://github.com/z-hao-wang/genetic-js#readme - Issues: https://github.com/z-hao-wang/genetic-js/issues - npm.io page: https://npm.io/package/genetic-nodejs-multithread ## Recent versions - 0.3.7 (latest) — 2020-01-12 - 0.3.6 — 2020-01-12 - 0.3.5 — 2019-12-29 - 0.3.4 — 2019-12-29 - 0.3.3 — 2019-12-28 - 0.3.2 — 2019-12-28 - 0.3.1 — 2019-12-28 - 0.3.0 — 2019-04-11 ## README # Genetic-multithread.js Advanced genetic and evolutionary algorithm library written in Javascript by [Sub Protocol](http://subprotocol.com/). ### 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 ```bash npm install genetic-nodejs-multithread ``` ## Population 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](#optimizer) section below | select1(population) | Individual | Yes | See [Selection](#selection) section below | select2(population) | Individual | Optional | Selects a pair of individuals from a population. [Selection](#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 ```javascript 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: ```bash 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. --- _Source: https://npm.io/package/genetic-nodejs-multithread · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._