MOP updated

d9w · Apr 12, 2024 · bd8fec3 · bd8fec3
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diff --git a/4_moo/NSGA-II.ipynb b/4_moo/NSGA-II.ipynb
@@ -340,7 +340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

diff --git a/4_moo/README.md b/4_moo/README.md
@@ -5,9 +5,9 @@ In this class, we look at multi-objective optimization and multi-objective
 evolutionary algoritms (MOEAs). We specifically look at an example of NSGA-II, a
 well-known MOEA, on the Travelling Salesman problem.
 
-[Multi-objective Optimization slides](https://d9w.github.io/evolution/3_moo/1_multiobjective.html)
+[Multi-objective Optimization slides](https://d9w.github.io/evolution/4_moo/1_multiobjective.html)
 
-[NSGA-II notebook](https://github.com/d9w/evolution/blob/master/3_moo/NSGA-II.ipynb)
+[NSGA-II notebook](https://github.com/d9w/evolution/blob/master/4_moo/NSGA-II.ipynb)
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/d9w/evolution/blob/master/3_moo/NSGA-II.ipynb)