{"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/comprehensive-feature-based-landscape","title":"Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco","arxiv_id":"1708.05258","date":"2017-08-17","proceeding":null,"authors":["Pascal Kerschke"],"abstract":"Choosing the best-performing optimizer(s) out of a portfolio of optimization\nalgorithms is usually a difficult and complex task. It gets even worse, if the\nunderlying functions are unknown, i.e., so-called Black-Box problems, and\nfunction evaluations are considered to be expensive. In the case of continuous\nsingle-objective optimization problems, Exploratory Landscape Analysis (ELA) -\na sophisticated and effective approach for characterizing the landscapes of\nsuch problems by means of numerical values before actually performing the\noptimization task itself - is advantageous. Unfortunately, until now it has\nbeen quite complicated to compute multiple ELA features simultaneously, as the\ncorresponding code has been - if at all - spread across multiple platforms or\nat least across several packages within these platforms.\n  This article presents a broad summary of existing ELA approaches and\nintroduces flacco, an R-package for feature-based landscape analysis of\ncontinuous and constrained optimization problems. Although its functions\nneither solve the optimization problem itself nor the related \"Algorithm\nSelection Problem (ASP)\", it offers easy access to an essential ingredient of\nthe ASP by providing a wide collection of ELA features on a single platform -\neven within a single package. In addition, flacco provides multiple\nvisualization techniques, which enhance the understanding of some of these\nnumerical features, and thereby make certain landscape properties more\ncomprehensible. On top of that, we will introduce the package's build-in, as\nwell as web-hosted and hence platform-independent, graphical user interface\n(GUI), which facilitates the usage of the package - especially for people who\nare not familiar with R - making it a very convenient toolbox when working\ntowards algorithm selection of continuous single-objective optimization\nproblems.","url_abs":"http://arxiv.org/abs/1708.05258v1","url_pdf":"http://arxiv.org/pdf/1708.05258v1.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":"comprehensive-feature-based-landscape","repo_url":"https://github.com/kerschke/flacco","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"comprehensive-feature-based-landscape","repo_url":"https://github.com/Reiyan/pflacco","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"comprehensive-feature-based-landscape","repo_url":"https://github.com/ryojitanabe/as_bbo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"comprehensive-feature-based-landscape","repo_url":"https://github.com/ryojitanabe/ela_drframework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.05258","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}