Results for genetic-algorithm-for-feature-selection

This Python code implements a genetic algorithm (GA) for feature selection. Feature selection is an important step in machine learning where irrelevant or redundant features are identified and removed from the dataset, improving model performance and redu

JWA implementation (supports all JWS algorithms)

A library for obtaining browser versions with their maximum supported Baseline feature set and Widely Available status.

An implementation of the Unicode Line Breaking Algorithm (UAX #14)

Implementation of JSON Web Signatures

Martinez polygon clipping algorithm, does boolean operation on polygons (multipolygons, polygons with holes etc): intersection, union, difference, xor

A tiny and fast selection algorithm in JavaScript.

css-line-break ==============

Multiprocessing genetic algorithm implementation library

A collection of algorithms and utilities for analyzing and selecting maximally distinct colors. Now includes a unified pickDistinctColors API for easy color selection.

Animated transitions for D3 selections.

An implementation of the Unicode Line Breaking Algorithm (UAX #14)

TypeScript definitions for d3-selection

Data-driven DOM manipulation: select elements and join them to data.

Implementation of genetic algorithms for nodejs and browser

Takes a complex (i.e. self-intersecting) geojson polygon, and breaks it down into its composite simple, non-self-intersecting one-ring polygons.

This package provides the list feature for Lexical.

Minimal implementation of Cardano coin selection algorithms (see [CIP-2](https://cips.cardano.org/cips/cip2/)) developed solely for Trezor Suite. Under the hood it leverages Cardano Serialization Lib via WASM module.

Kendo UI TreeView for Angular

Toggle current selected content in browser

ProseMirror's rowspan/colspan tables component

This package contains utilities and helpers for handling Lexical selection.

Detect the indentation of code

The fastest and smallest JavaScript polygon triangulation library for your WebGL apps

== DESCRIPTION: Charlie is a library for genetic algorithms (GA) and genetic programming (GP). == FEATURES: - Quickly develop GAs by combining several parts (genotype, selection, crossover, mutation) provided by the library. - Sensible defaults are provided with any genotype, so often you only need to define a fitness function. - Easily replace any of the parts by your own code. - Test different strategies in GA, and generate reports comparing them. Example report: http://charlie.rubyforge.org/example_report.html == INSTALL: * sudo gem install charlie == EXAMPLES: This example solves a TSP problem (also quiz #142): N=5 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b} class TSP < PermutationGenotype(CITIES.size) def fitness d=0 (genes + [genes[0]]).each_cons(2){|a,b| a,b=CITIES[a],CITIES[b] d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 ) } -d # lower distance -> higher fitness. end use EdgeRecombinationCrossover, InversionMutator end Population.new(TSP,20).evolve_on_console(50) This example finds a polynomial which approximates cos(x) class Cos < TreeGenotype([proc{3*rand-1.5},:x], [:-@], [:+,:*,:-]) def fitness -[0,0.33,0.66,1].map{|x| (eval_genes(:x=>x) - Math.cos(x)).abs }.max end use TournamentSelection(4) end Population.new(Cos).evolve_on_console(500)