[{"title":"Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr","year":"2018","publication_identifier":{"isbn":["978-1-4503-5764-7"]},"author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","id":"102979"}],"date_updated":"2023-12-13T10:46:04Z","publication_status":"published","series_title":"GECCO ’18","language":[{"iso":"eng"}],"doi":"10.1145/3205651.3208312","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"text":"Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.","lang":"eng"}],"extern":"1","date_created":"2023-11-14T15:58:56Z","type":"conference","keyword":["evolutionary optimization","performance assessment","software-tools"],"department":[{"_id":"819"}],"status":"public","page":"1350–1356","_id":"48867","publisher":"Association for Computing Machinery","user_id":"102979","citation":{"apa":"Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1350–1356. <a href=\"https://doi.org/10.1145/3205651.3208312\">https://doi.org/10.1145/3205651.3208312</a>","ieee":"J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2018, pp. 1350–1356, doi: <a href=\"https://doi.org/10.1145/3205651.3208312\">10.1145/3205651.3208312</a>.","short":"J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1350–1356.","chicago":"Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package Ecr.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1350–1356. GECCO ’18. New York, NY, USA: Association for Computing Machinery, 2018. <a href=\"https://doi.org/10.1145/3205651.3208312\">https://doi.org/10.1145/3205651.3208312</a>.","mla":"Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package Ecr.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2018, pp. 1350–1356, doi:<a href=\"https://doi.org/10.1145/3205651.3208312\">10.1145/3205651.3208312</a>.","ama":"Bossek J. Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO ’18. Association for Computing Machinery; 2018:1350–1356. doi:<a href=\"https://doi.org/10.1145/3205651.3208312\">10.1145/3205651.3208312</a>","bibtex":"@inproceedings{Bossek_2018, place={New York, NY, USA}, series={GECCO ’18}, title={Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}, DOI={<a href=\"https://doi.org/10.1145/3205651.3208312\">10.1145/3205651.3208312</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob}, year={2018}, pages={1350–1356}, collection={GECCO ’18} }"},"place":"New York, NY, USA"},{"status":"public","page":"1187–1193","_id":"48863","publisher":"Association for Computing Machinery","user_id":"102979","citation":{"apa":"Bossek, J. (2017). Ecr 2.0: A Modular Framework for Evolutionary Computation in R. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1187–1193. <a href=\"https://doi.org/10.1145/3067695.3082470\">https://doi.org/10.1145/3067695.3082470</a>","mla":"Bossek, Jakob. “Ecr 2.0: A Modular Framework for Evolutionary Computation in R.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2017, pp. 1187–1193, doi:<a href=\"https://doi.org/10.1145/3067695.3082470\">10.1145/3067695.3082470</a>.","ieee":"J. Bossek, “Ecr 2.0: A Modular Framework for Evolutionary Computation in R,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2017, pp. 1187–1193, doi: <a href=\"https://doi.org/10.1145/3067695.3082470\">10.1145/3067695.3082470</a>.","chicago":"Bossek, Jakob. “Ecr 2.0: A Modular Framework for Evolutionary Computation in R.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1187–1193. GECCO ’17. New York, NY, USA: Association for Computing Machinery, 2017. <a href=\"https://doi.org/10.1145/3067695.3082470\">https://doi.org/10.1145/3067695.3082470</a>.","short":"J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2017, pp. 1187–1193.","ama":"Bossek J. Ecr 2.0: A Modular Framework for Evolutionary Computation in R. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO ’17. Association for Computing Machinery; 2017:1187–1193. doi:<a href=\"https://doi.org/10.1145/3067695.3082470\">10.1145/3067695.3082470</a>","bibtex":"@inproceedings{Bossek_2017, place={New York, NY, USA}, series={GECCO ’17}, title={Ecr 2.0: A Modular Framework for Evolutionary Computation in R}, DOI={<a href=\"https://doi.org/10.1145/3067695.3082470\">10.1145/3067695.3082470</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob}, year={2017}, pages={1187–1193}, collection={GECCO ’17} }"},"place":"New York, NY, USA","year":"2017","title":"Ecr 2.0: A Modular Framework for Evolutionary Computation in R","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek"}],"publication_identifier":{"isbn":["978-1-4503-4939-0"]},"date_updated":"2023-12-13T10:45:05Z","publication_status":"published","series_title":"GECCO ’17","language":[{"iso":"eng"}],"doi":"10.1145/3067695.3082470","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"text":"The novel R package ecr (version 2), short for Evolutionary Computation in R, provides a comprehensive collection of building blocks for constructing powerful evolutionary algorithms for single- and multi-objective continuous and combinatorial optimization problems. It allows to solve standard optimization tasks with few lines of code using a black-box approach. Moreover, rapid prototyping of non-standard ideas is possible via an explicit, white-box approach. This paper describes the design principles of the package and gives some introductory examples on how to use the package in practise.","lang":"eng"}],"extern":"1","date_created":"2023-11-14T15:58:55Z","keyword":["evolutionary optimization","software-tools"],"type":"conference","department":[{"_id":"819"}]},{"publication_identifier":{"isbn":["9781450311779"]},"author":[{"last_name":"Bischl","first_name":"Bernd","full_name":"Bischl, Bernd"},{"full_name":"Mersmann, Olaf","first_name":"Olaf","last_name":"Mersmann"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Preuß","first_name":"Mike","full_name":"Preuß, Mike"}],"year":"2012","title":"Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning","date_updated":"2023-10-16T13:48:48Z","language":[{"iso":"eng"}],"series_title":"GECCO ’12","doi":"10.1145/2330163.2330209","publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","abstract":[{"text":"The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop.","lang":"eng"}],"date_created":"2023-08-04T15:51:56Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","keyword":["machine learning","exploratory landscape analysis","fitness landscape","benchmarking","evolutionary optimization","bbob test set","algorithm selection"],"status":"public","_id":"46396","publisher":"Association for Computing Machinery","page":"313–320","user_id":"15504","citation":{"short":"B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320.","chicago":"Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>.","apa":"Bischl, B., Mersmann, O., Trautmann, H., &#38; Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 2012, pp. 313–320, doi: <a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","ama":"Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>","bibtex":"@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY, USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}, DOI={<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320}, collection={GECCO ’12} }","mla":"Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2012, pp. 313–320, doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>."},"place":"New York, NY, USA"},{"date_updated":"2023-10-16T13:54:34Z","year":"2011","title":"Exploratory Landscape Analysis","publication_identifier":{"isbn":["9781450305570"]},"author":[{"full_name":"Mersmann, Olaf","first_name":"Olaf","last_name":"Mersmann"},{"last_name":"Bischl","first_name":"Bernd","full_name":"Bischl, Bernd"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Preuss, Mike","first_name":"Mike","last_name":"Preuss"},{"full_name":"Weihs, Claus","first_name":"Claus","last_name":"Weihs"},{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"}],"doi":"10.1145/2001576.2001690","series_title":"GECCO ’11","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Exploratory Landscape Analysis subsumes a number of techniques employed to obtain knowledge about the properties of an unknown optimization problem, especially insofar as these properties are important for the performance of optimization algorithms. Where in a first attempt, one could rely on high-level features designed by experts, we approach the problem from a different angle here, namely by using relatively cheap low-level computer generated features. Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection."}],"publication":"Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation","type":"conference","keyword":["exploratory landscape analysis","evolutionary optimization","fitness landscape","benchmarking","BBOB test set"],"department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T15:58:22Z","status":"public","user_id":"15504","page":"829–836","_id":"46401","publisher":"Association for Computing Machinery","citation":{"ama":"Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G. Exploratory Landscape Analysis. In: <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’11. Association for Computing Machinery; 2011:829–836. doi:<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>","bibtex":"@inproceedings{Mersmann_Bischl_Trautmann_Preuss_Weihs_Rudolph_2011, place={New York, NY, USA}, series={GECCO ’11}, title={Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>}, booktitle={Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Preuss, Mike and Weihs, Claus and Rudolph, Günter}, year={2011}, pages={829–836}, collection={GECCO ’11} }","mla":"Mersmann, Olaf, et al. “Exploratory Landscape Analysis.” <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2011, pp. 829–836, doi:<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>.","chicago":"Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. “Exploratory Landscape Analysis.” In <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 829–836. GECCO ’11. New York, NY, USA: Association for Computing Machinery, 2011. <a href=\"https://doi.org/10.1145/2001576.2001690\">https://doi.org/10.1145/2001576.2001690</a>.","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, G. Rudolph, in: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2011, pp. 829–836.","apa":"Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., &#38; Rudolph, G. (2011). Exploratory Landscape Analysis. <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 829–836. <a href=\"https://doi.org/10.1145/2001576.2001690\">https://doi.org/10.1145/2001576.2001690</a>","ieee":"O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph, “Exploratory Landscape Analysis,” in <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 2011, pp. 829–836, doi: <a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>."},"place":"New York, NY, USA"},{"_id":"46405","publisher":"Springer-Verlag","series_title":"PPSN’10","language":[{"iso":"eng"}],"page":"73–82","user_id":"15504","publication_identifier":{"isbn":["3642158439"]},"author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike"}],"status":"public","title":"Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis","year":"2010","date_updated":"2023-10-16T13:55:43Z","place":"Berlin, Heidelberg","date_created":"2023-08-04T16:02:28Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","keyword":["benchmarking","multidimensional scaling","consensus ranking","evolutionary optimization","BBOB test set"],"citation":{"apa":"Mersmann, O., Preuss, M., &#38; Trautmann, H. (2010). Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 73–82.","ieee":"O. Mersmann, M. Preuss, and H. Trautmann, “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis,” in <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 2010, pp. 73–82.","short":"O. Mersmann, M. Preuss, H. Trautmann, in: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 73–82.","chicago":"Mersmann, Olaf, Mike Preuss, and Heike Trautmann. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” In <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 73–82. PPSN’10. Berlin, Heidelberg: Springer-Verlag, 2010.","mla":"Mersmann, Olaf, et al. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, Springer-Verlag, 2010, pp. 73–82.","ama":"Mersmann O, Preuss M, Trautmann H. Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In: <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>. PPSN’10. Springer-Verlag; 2010:73–82.","bibtex":"@inproceedings{Mersmann_Preuss_Trautmann_2010, place={Berlin, Heidelberg}, series={PPSN’10}, title={Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis}, booktitle={Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I}, publisher={Springer-Verlag}, author={Mersmann, Olaf and Preuss, Mike and Trautmann, Heike}, year={2010}, pages={73–82}, collection={PPSN’10} }"},"publication":"Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I","abstract":[{"lang":"eng","text":"We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the ’best’ one? and the second one: which algorithm should I use for my real world problem? Both are connected and neither is easy to answer. We present methods which can be used to analyse the raw data of a benchmark experiment and derive some insight regarding the answers to these questions. We employ the presented methods to analyse the BBOB’09 benchmark results and present some initial findings."}]}]