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https://github.com/se2p/sbse2023

Jupyter notebooks accompanying the Search-Based Software Engineering Course WS23/24
https://github.com/se2p/sbse2023

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Jupyter notebooks accompanying the Search-Based Software Engineering Course WS23/24

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# Search-Based Software Engineering Course WS23/24



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](https://jupyter.org/install).

If you need help with setting up Jupyter, here's a tutorial on [how to install jupyter notebook on your machine](https://www.dataquest.io/blog/jupyter-notebook-tutorial/).



## 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](rendered/Random%20and%20Local%20Search.md)



## Chapter 2: Evolutionary Search (Part 1)



This chapter covers basic evolutionary strategies and genetic algorithms.

[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%201.md)



## 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.

[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%202.md)



## 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](rendered/Multi-Objective%20Optimisation%20-%20Part%201.md)



## 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](rendered/Multi-Objective%20Optimisation%20-%20Part%202.md)



## Chapter 6: Search-based Test Generation (Part 1)



This chapter looks at how the problem of test input generation can be cast

as a search problem, and how to automatically instrument programs for

fitness generation.

[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%201.md)



## Chapter 7: Search-based Test Generation (Part 2)



This chapter continues whole test suite generation, and then moves on to

many objective optimisation for test generation.



[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%202.md)



## Chapter 8: Genetic Programming



This chapter introduces classic genetic programming for scenarios assuming

type closure, and applies this to symbolic regression and spectrum-based

fault localisation. It also looks at grammatical evolution and automated program repair.



[Markdown Export](rendered/Genetic%20Programming.md)



## Chapter 9: Neuroevolution



This chapter introduces the field of Neuroevolution in which evolutionary algorithms are used to optimise artificial

neural networks. We start with the definition of neural networks and the pole balancing problem, a popular reinforcement

learning task which will be solved using two different Neuroevolution algorithms. The two algorithms, Symbiotic Adaptive

Neuroevolution (SANE) and Cooperative Synapse Neuroevolution (CoSyNE), respectively, evolve a population of hidden

neurons and connection weights.



[Markdown Export](rendered/Neuroevolution.md)



## Chapter 10: Parameter Tuning and Parameter Control



This chapter considers how to choose values for the many parameters that we

have introduced in our evolutionary algorithms, how to optimise these

values, and how to adapt them to new problems.



[Markdown Export](rendered/Parameter%20Control%20and%20Adaptation.md)



## Chapter 11: Advanced Evolutionary Algorithms



This chapter considers several advanced variants of the evolutionary

algorithms we have discussed in previous chapter: Memetic algorithms combine

global and local search; island model GAs divide the population of a GA into

independent subpopulations; estimation of distribution algorithms try to

explicitly optimise the probability distribution that is otherwise

implicitly represented by the population; differential evolution uses novel

search operators unlike the ones we have used in standard GAs; hyper

heuristics try to combine different heuristics to adapt to the problem at hand.



[Markdown Export](rendered/Advanced%20Evolutionary%20Algorithms.md)



## Chapter 12: Swarm Optimisation



This chapter briefly introduces two swarm optimisation techniques: Ant colony optimisation

tries to imitate the stigmergic communication of ants for the purpose of

optimisation. Particle swarm optimisation simulates the swarm behaviour of

birds or fish.



[Markdown Export](rendered/Swarm%20Optimisation.md)