# Evolutionary algorithm Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/evolutionary-algorithm
> Markdown URL: https://aitinkerers.org/technologies/evolutionary-algorithm.md
> Technology record last updated: 2026-03-20T20:25:12Z
> Generated: 2026-09-22T05:45:49Z

A population-based metaheuristic that uses Darwinian mechanisms like mutation and crossover to solve complex global optimization problems.

Evolutionary algorithms (EAs) mimic biological selection to navigate vast search spaces where traditional calculus-based methods fail. By maintaining a population of candidate solutions (phenotypes), the system applies stochastic operators—specifically mutation, recombination, and tournament selection—to iteratively refine performance. This methodology powers critical engineering feats, from NASA's ST5 spacecraft antenna design to high-frequency trading strategies and deep neural architecture search (NAS). EAs excel in multi-objective optimization (MOO), allowing stakeholders to balance competing constraints like cost and structural integrity without requiring a differentiable objective function.

- Official technology site: https://www.nature.com/articles/s42256-020-00278-y
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Good Vibes and Bad Sheep: An Evolutionary Algorithm](https://montreal.aitinkerers.org/talks/rsvp_YI9Ghd5wRU0)

An evolutionary algorithm where sheep learn to find food and water in an automatically generated 3D world. This started as an experiment to see how quickly I could replicate my final year undergraduate computer science project (from 2017) using modern coding practices.

- Event context: AI Tinkerers Montreal - March Demo Night — 2026-03-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_YI9Ghd5wRU0

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