zero-downtime deployments
dchart-pie-crossover-one-value
An email&html templating crossover
Bridging the Gap between Frontend and Backend.
A window detection library for CrossOver
Crossover Tdlib with Telegraf framework – easy way to create Telegram bots with Telegram personal accounts
Some gene animations
MCP server for Safrapay — Banco Safra's acquirer (B2B-banking crossover, mid-to-large BR merchants). Credit, Pix, boleto + split, statement, tokenization.
Genetic-Pareto prompt optimizer to evolve system prompts from a few rollouts with modular support and intelligent crossover
Sailing polar diagrams with live performance calculations for SignalK. Includes sail wardrobe management, polar tables per sail combination, crossover charts, and real-time performance metrics.
This library optimizes your agent's performance by way of an evolutionary algorithm, a model of biological evolution: rank fitness, crossover of best performing, mutation, run next generation.
Git-based evolutionary algorithm design engine. Evolves code via LLM-driven mutation, crossover, and reflection on any git repository.
Audio filter design utilities (biquad PEQ/shelves, crossover families, RIAA) for Node.js
A modular JavaScript API for programming with genetic algorithms.
Multiprocessing genetic algorithm implementation library
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Audio filters – weighting, auditory, analog, speech, eq, effects
Genetic programming helpers & strategies (tree based & multi-expression programming)
Date/time, IANA timezones, leap seconds, TAI/UTC conversions, calendar with settable Julian/Gregorian switchover
Autonomous trading plugin with LLM-powered strategies for Solana
A collection of trading indicators and strategies
The easiest way to create a new Backtest Kit trading bot project. Like create-react-app, but for algorithmic trading with LLM integration and technical analysis.
A library which exposes a genetic algorithm to solve (one of) the [timetabling problem](http://web.csulb.edu/~obenli/DSS/node2.html).
list of golden cross stocks. list of death cross stocks. STE. CLI tool.
A library for implementing and executing evolutionary algorithms with customizable models.
Library for solving genetic algorithm problems
Procedural macros for pymoors project
A genetic algorithm implementation
A derivation engine for L-Systems (ABOP compliant).
A flexible, high-performance genetic algorithm library written in Rust
Automatic equalization for speakers, headphones and rooms!
Predictive Coding Actor-Critic reinforcement learning framework — pure Rust, zero ML dependencies
GPU acceleration for mathematical computations
A fully extensible Rust framework for using paralyzed genetic algorithms to solve problems.
Multi-objective genetic optimization algorithm NSGA-II
Technical analysis indicators, streaming signals, and a single-asset backtesting engine for Rust
Sample Client-Server App using TCP Socket.
Basic Agent for CrossOver Monitoring Server
wwine is a a wine(1) wrapper. It wraps various flavours of wine (including vanilla wine and crossover) into a single unified interface, complete with full bottle support for all of them (including vanilla wine).
== 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)
I sometimes get a little descriptive with my variable names, so when you're doing a lot of work specifically with one object, it gets especially ugly and repetetive, making the code harder to read than it needs to be: @contract_participants_on_drugs.contract_id = params[:contract_id] @contract_participants_on_drugs.participant_name = params[:participant_name] @contract_participants_on_drugs.drug_conviction = DrugConvictions.find(:wtf => 'this is getting ridiculous') ... And so on. It gets ridiculous. Utility Belt implements a with(object) method via a change to Object: class Object #utility belt implementation def with(object, &block) object.instance_eval &block end end Unfortunately, that just executes the block in the context of the object, so there isn't any crossover, nor can you perform assignments with attr_accessors (that I was able to do, anyway). So, here's With.object() to fill the void. With.object(@foo) do a = "wtf" b = "this is not as bad" end In the above example, @foo.a and @foo.b are the variables getting set. If you prefer, you can require 'with_on_object' instead and use the notation with(object) do ... end. The tests in the /test directory offer more examples of what's been implemented and tested so far (except where noted - namely performing assignment to a variable that was declared outside the block, and is not on @foo). Not everything is working yet, but it works for the simplest, most common cases I've run up against. More complex tests are on the way, along with code to make them pass. Special thanks to Reg Braithwaite, for help and ideas along the way.
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