# MorvanZhou/Evolutionary-Algorithm

Evolutionary Algorithm using Python, 莫烦Python 中文AI教学

Repository: https://github.com/MorvanZhou/Evolutionary-Algorithm
Canonical: https://ross.abutalabs.com/products/evolutionary-algorithm
Homepage: https://mofanpy.com/tutorials/machine-learning/evolutionary-algorithm/
Language: Python
License: MIT
License Family: permissive
Topics: evolutionary-algorithm, genetic-algorithm, neuroevolution, microbial-genetic-algorithm, travel-sale-problem, evolution-strategy, es, reinforcement-learning, neural-network, microbial-ga, neat, neural-nets, python, travel-sales-problem, nes, evolution-strategies, openai, distributed-es, machine-learning, tutorial
Last push: 2023-11-26T18:28:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3336, "days_push": 1011, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1233, forks 619 (observed 2026-08-28T04:04:04.582774+00:00)

## What it is
A collection of Python tutorial scripts and visualizations teaching evolutionary algorithms, including Genetic Algorithm, Evolution Strategy, NES, and NEAT, with Chinese video/text lessons from Mofan Python. It is educational example code rather than a production library (the author's MEvo package serves that role).

## Use cases
- learn how genetic algorithms work with runnable python examples
- see a visualization of solving the traveling salesman problem with a GA
- understand evolution strategies like (1+1)-ES and NES
- learn NEAT for supervised and reinforcement learning
- study distributed evolution strategy with neural networks
- find tutorial material for teaching evolutionary computation

## When to choose
- you are learning or teaching evolutionary algorithms and want simple, visual, runnable examples
- you prefer tutorial code with accompanying video/text explanations
- you want a starting point before using a real EA library like MEvo

## When to avoid
- you need a production-ready, maintained evolutionary algorithm library
- you need performant or parallel GA/ES implementations for real workloads
- you cannot read Chinese and need full tutorial context

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, simulation, data-visualization
- domain: machine-learning, artificial-intelligence, tutorials, education, reinforcement-learning
- platform: python, cross-platform
- tags: genetic-algorithm, evolution-strategy, neat, neuroevolution, tutorial-code, chinese-language, traveling-salesman, numpy

## Member repositories
- MorvanZhou/Evolutionary-Algorithm (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.582774+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:13.656886+00:00, confidence not recorded.
  - readme: https://github.com/MorvanZhou/Evolutionary-Algorithm (fetched 2026-08-28T04:04:04.582774+00:00, sha 6eb3d8c43960)
  - homepage: https://mofanpy.com/tutorials/machine-learning/evolutionary-algorithm/ (fetched 2026-08-29T12:22:02.992560+00:00, sha 77d297939888)
  - site_page: https://mofanpy.com/about (fetched 2026-08-29T12:22:03.001555+00:00, sha 828465c2b53d)
- Data as of 2026-08-30T08:39:29.467469+00:00.