Search-Based Software Engineering Course WS24/25

This repository contains the code examples and content of the lectures. I will be uploading rendered versions as PDF files to StudIP, and include rendered Markdown versions in this repository. If you want to run the notebooks yourself, you will need to install Jupyter. If you need help with setting up Jupyter, here's a tutorial on how to install jupyter notebook on your machine.

Chapter 1: Random and Local Search

The first chapter covers the coding examples from the first two weeks, on basic random search and local search algorithms. Markdown Export

Chapter 2: Evolutionary Search (Part 1)

This chapter covers basic evolutionary strategies and genetic algorithms. Markdown Export

Chapter 3: Evolutionary Search (Part 2)

This chapter looks into the various search operators of a genetic algorithm: Survivor selection, parent selection, crossover, mutation, and the population itself. We also look at memetic algorithms, which combine global and local search.

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Chapter 4: Multi-Objective Optimisation (Part 1)

This chapter covers the basics of Pareto optimality, NSGA-II, and comparison of multi-objective search algorithms. Markdown Export

Chapter 5: Multi-Objective Optimisation (Part 2)

This chapter covers several alternative multi-objective search algorithms: A random baseline, PAES, SPEA2, TwoArchives, and SMS-EMOA. Markdown Export