{"id":23792478,"url":"https://github.com/se2p/sbse2024","last_synced_at":"2025-07-08T10:41:19.130Z","repository":{"id":261239957,"uuid":"883709256","full_name":"se2p/sbse2024","owner":"se2p","description":null,"archived":false,"fork":false,"pushed_at":"2024-12-18T14:50:18.000Z","size":16693,"stargazers_count":4,"open_issues_count":1,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2024-12-18T15:42:20.267Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/se2p.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-05T12:46:52.000Z","updated_at":"2024-12-18T14:50:22.000Z","dependencies_parsed_at":"2024-11-05T13:53:57.474Z","dependency_job_id":"4f4ac48b-73a8-41c4-a0a3-787522fb62f8","html_url":"https://github.com/se2p/sbse2024","commit_stats":null,"previous_names":["se2p/sbse2024"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/se2p%2Fsbse2024","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/se2p%2Fsbse2024/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/se2p%2Fsbse2024/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/se2p%2Fsbse2024/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/se2p","download_url":"https://codeload.github.com/se2p/sbse2024/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":232115683,"owners_count":18474815,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-01T18:35:17.905Z","updated_at":"2025-07-08T10:41:19.108Z","avatar_url":"https://github.com/se2p.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Search-Based Software Engineering Course WS24/25\n\n\nThis repository contains the code examples and content of the lectures. I\nwill be uploading rendered versions as PDF files to StudIP, and include\nrendered Markdown versions in this repository. If you want to run the\nnotebooks yourself, you will need to install [Jupyter](https://jupyter.org/install).\nIf 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/).\n\n\n## Chapter 1: Random and Local Search\n\nThe first chapter covers the coding examples from the first two weeks, on basic random search and local search algorithms.\n[Markdown Export](rendered/Random%20and%20Local%20Search.md)\n\n\n## Chapter 2: Evolutionary Search (Part 1)\n\nThis chapter covers basic evolutionary strategies and genetic algorithms.\n[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%201.md)\n\n\n## Chapter 3: Evolutionary Search (Part 2)\n\nThis chapter looks into the various search operators of a genetic algorithm:\nSurvivor selection, parent selection, crossover, mutation, and the\npopulation itself. We also look at memetic algorithms, which combine\nglobal and local search.\n\n[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%202.md)\n\n\n## Chapter 4: Multi-Objective Optimisation (Part 1)\n\nThis chapter covers the basics of Pareto optimality, NSGA-II, and comparison\nof multi-objective search algorithms.\n[Markdown Export](rendered/Multi-Objective%20Optimisation%20-%20Part%201.md)\n\n\n## Chapter 5: Multi-Objective Optimisation (Part 2)\n\nThis chapter covers several alternative multi-objective search algorithms:\nA random baseline, PAES, SPEA2, TwoArchives, and SMS-EMOA.\n[Markdown Export](rendered/Multi-Objective%20Optimisation%20-%20Part%202.md)\n\n\n## Chapter 6: Search-based Test Generation (Part 1)\n\nThis chapter looks at how the problem of test input generation can be cast\nas a search problem, and how to automatically instrument programs for\nfitness generation.\n[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%201.md)\n\n\n## Chapter 7: Search-based Test Generation (Part 2)\n\nThis chapter continues whole test suite generation, and then moves on to\nmany objective optimisation for test generation.\n\n[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%202.md)\n\n\n## Chapter 8: Parameter Tuning and Parameter Control\n\nThis chapter considers how to choose values for the many parameters that we\nhave introduced in our evolutionary algorithms, how to optimise these\nvalues, and how to adapt them to new problems.\n\n[Markdown Export](rendered/Parameter%20Control%20and%20Adaptation.md)\n\n\n## Chapter 9: Genetic Programming (Part 1)\n\nThis chapter introduces classic genetic programming for scenarios assuming\ntype closure, and applies this to symbolic regression and spectrum-based\nfault localisation. \n\n[Markdown Export](rendered/Genetic%20Programming%20-%20Part%201.md)\n\n## Chapter 13: Neuroevolution\n\nThis chapter introduces the field of Neuroevolution in which evolutionary algorithms are used to optimise artificial\nneural networks. We start with the definition of neural networks and the pole balancing problem, a popular reinforcement\nlearning task which is then solved using two different Neuroevolution algorithms: Symbiotic Adaptive\nNeuroevolution (SANE) and Cooperative Synapse Neuroevolution (CoSyNE).\n\n[Markdown Export](rendered/Neuroevolution.md)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse2p%2Fsbse2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fse2p%2Fsbse2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse2p%2Fsbse2024/lists"}