---
title: "Solve Multi-Objective Problem using NSGA-II and DEAP in Python"
type: source
tags: [optimisation, nsga-ii, tutorial, multi-objective, supporting]
authors: [Lee Chi Ming]
year: 2019
venue: "Medium (blog post), 24 Apr 2019"
kind: webpage
raw_path: "raw/Predictive process monitoring/Solve Multi-Objective Problem using NSGA-II and DEAP in Python.pdf"
created: 2026-04-13
updated: 2026-04-13
key_claims:
  - Tutorial introduction to NSGA-II (Deb, Pratap, Agarwal, Meyarivan) and its use via the DEAP Python package.
  - Explains Pareto dominance, non-dominated sorting, and crowding distance.
---

# Lee 2019 — NSGA-II + DEAP Tutorial (Medium blog)

Reference material (not a research paper) — a short Medium tutorial on **NSGA-II** (Non-dominated Sorting Genetic Algorithm II) and the Python DEAP package. Filed because it sits in the PPM folder and appears to be background material for multi-objective hyperparameter optimisation work like [[sources/2018-difrancescomarino-genetic-hpo-ppm]].

## Why it's in the wiki
Supporting reference for multi-objective optimisation techniques used in PPM hyperparameter tuning.

## Connections
**Related sources:** [[sources/2018-difrancescomarino-genetic-hpo-ppm]] (uses GA/HPO, relevant context for NSGA-II).