{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hyper-parameter-tuning-for-the-1-ga","title":"Hyper-Parameter Tuning for the (1+(λ,λ)) GA","arxiv_id":"1904.04608","date":"2019-04-09","proceeding":null,"authors":["Nguyen Dang","Carola Doerr"],"abstract":"It is known that the $(1+(\\lambda,\\lambda))$~Genetic Algorithm (GA) with\nself-adjusting parameter choices achieves a linear expected optimization time\non OneMax if its hyper-parameters are suitably chosen. However, it is not very\nwell understood how the hyper-parameter settings influences the overall\nperformance of the $(1+(\\lambda,\\lambda))$~GA. Analyzing such multi-dimensional\ndependencies precisely is at the edge of what running time analysis can offer.\nTo make a step forward on this question, we present an in-depth empirical study\nof the self-adjusting $(1+(\\lambda,\\lambda))$~GA and its hyper-parameters. We\nshow, among many other results, that a 15\\% reduction of the average running\ntime is possible by a slightly different setup, which allows non-identical\noffspring population sizes of mutation and crossover phase, and more\nflexibility in the choice of mutation rate and crossover bias --a\ngeneralization which may be of independent interest. We also show indication\nthat the parametrization of mutation rate and crossover bias derived by\ntheoretical means for the static variant of the $(1+(\\lambda,\\lambda))$~GA\nextends to the non-static case.","url_abs":"http://arxiv.org/abs/1904.04608v1","url_pdf":"http://arxiv.org/pdf/1904.04608v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hyper-parameter-tuning-for-the-1-ga","repo_url":"https://github.com/ndangtt/1LLGA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}