https://github.com/se2p/sbse2020
https://github.com/se2p/sbse2020
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/se2p/sbse2020
- Owner: se2p
- Created: 2020-11-10T17:31:38.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-01-27T09:59:18.000Z (over 5 years ago)
- Last Synced: 2025-02-21T13:30:34.076Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 12.6 MB
- Stars: 3
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Search-Based Software Engineering Course WS20/21
For now, the Jupyter notebooks are provided as is, and you will have to download and open them in Jupyter. Here's [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.
## Chapter 2: Evolutionary Search (Part 1)
This chapter covers basic evolutionary strategies and genetic algorithms.
## Chapter 3: Evolutionary Search (Part 2)
This chapter looks at alternative search operators for genetic algorithms
and their consequences on diversity and fitness.
## Chapter 4: Multi-Objective Evolutionary Algorithms
This chapter considers how to extend evolutionary search to more than one
objective function.
## Chapter 5: Search-Based Test Generation
This chapter looks at the basics of search-based test generation: How to
encode simple problems, what are fitness functions, and how can we adapt
search algorithms to work well on these problems.
## Chapter 6: Genetic Programming
This chapter introduces the basics of Genetic Programming, starting with
classical, untyped syntax trees, up to Grammatical Evolution.
## Chapter 7: Parameter Control and Adaptation
We have many algorithms to choose from, and each algorithm has multiple
parameters that need to be set. We mainly used some reasonably common
default values in previous chapters. In this chapter we consider the
question of how we can optimise these parameters, and control and adapt
them throughout the search.
## Chapter 8: Advanced Evolutionary Algorithms
This chapter covers more advanced versions of the evolutionary algorithms
we have covered, such as parallelised versions, memetic algorithms, as
well as differential evolution and the extension to estimation of
distribution algorithms.