A Hands-On Introduction to Multi-Objective Optimization

Dive into a hands-on Python tutorial exploring multi-objective optimization and Pareto fronts. Learn to tackle complex problems with conflicting objectives, such as balancing quality and cost in production. Apply newly acquired skills to the Knapsack problem, programming to minimize bag weight while maximizing content value. Gain practical insights into real-world applications spanning supply chain management, manufacturing, aircraft design, and therapeutic development. Work through interactive Jupyter notebooks, visualize decision spaces, and explore case studies in data science and machine learning. Develop a fundamental understanding of this powerful technique to assess its applicability in various projects and overcome limitations of single-parameter heuristics.

Introduction
MultiObjective Optimization
Objectives
About Me
Agenda Parts
Welcome Motivation
Play Button
Google Collab
Summary
Part 1 Challenge
Part 2 Challenge
Quick Summary
Application
Deep
Notebook
Notebook Setup
Deep Setup
Custom Functions
Knapsack Problem
Visualization
Final Result
Hack
Motivation
Case Studies
Case Study 1
Decision Space Animation
Data Science Example
Machine Learning Example
Summarize