{"quality_controlled":"1","month":"07","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"_id":"1349","page":"1163 - 1170","status":"public","publisher":"ACM","project":[{"call_identifier":"FP7","grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference 2016 ","ddc":["576"],"type":"conference","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"pubrep_id":"650","language":[{"iso":"eng"}],"ec_funded":1,"scopus_import":1,"publist_id":"5900","doi":"10.1145/2908812.2908909","title":"When non-elitism outperforms elitism for crossing fitness valleys","year":"2016","date_created":"2018-12-11T11:51:31Z","publication_status":"published","has_accepted_license":"1","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:50:03Z","day":"20","file":[{"date_updated":"2020-07-14T12:44:45Z","file_size":979026,"content_type":"application/pdf","date_created":"2018-12-12T10:16:27Z","creator":"system","file_name":"IST-2016-650-v1+1_p1163-oliveto.pdf","relation":"main_file","file_id":"5214","checksum":"a1896e39e4113f2711e46b435d5f3e69","access_level":"open_access"}],"author":[{"last_name":"Oliveto","first_name":"Pietro","full_name":"Oliveto, Pietro"},{"full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago","orcid":"0000-0003-2361-3953","last_name":"Paixao"},{"last_name":"Heredia","first_name":"Jorge","full_name":"Heredia, Jorge"},{"last_name":"Sudholt","first_name":"Dirk","full_name":"Sudholt, Dirk"},{"last_name":"Trubenova","first_name":"Barbora","orcid":"0000-0002-6873-2967","id":"42302D54-F248-11E8-B48F-1D18A9856A87","full_name":"Trubenova, Barbora"}],"file_date_updated":"2020-07-14T12:44:45Z","citation":{"ista":"Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. 2016. When non-elitism outperforms elitism for crossing fitness valleys. Proceedings of the Genetic and Evolutionary Computation Conference 2016 . GECCO: Genetic and evolutionary computation conference, 1163–1170.","short":"P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, B. Trubenova, in:, Proceedings of the Genetic and Evolutionary Computation Conference 2016 , ACM, 2016, pp. 1163–1170.","mla":"Oliveto, Pietro, et al. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” Proceedings of the Genetic and Evolutionary Computation Conference 2016 , ACM, 2016, pp. 1163–70, doi:10.1145/2908812.2908909.","ama":"Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. When non-elitism outperforms elitism for crossing fitness valleys. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016 . ACM; 2016:1163-1170. doi:10.1145/2908812.2908909","ieee":"P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, and B. Trubenova, “When non-elitism outperforms elitism for crossing fitness valleys,” in Proceedings of the Genetic and Evolutionary Computation Conference 2016 , Denver, CO, USA, 2016, pp. 1163–1170.","chicago":"Oliveto, Pietro, Tiago Paixao, Jorge Heredia, Dirk Sudholt, and Barbora Trubenova. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” In Proceedings of the Genetic and Evolutionary Computation Conference 2016 , 1163–70. ACM, 2016. https://doi.org/10.1145/2908812.2908909.","apa":"Oliveto, P., Paixao, T., Heredia, J., Sudholt, D., & Trubenova, B. (2016). When non-elitism outperforms elitism for crossing fitness valleys. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp. 1163–1170). Denver, CO, USA: ACM. https://doi.org/10.1145/2908812.2908909"},"oa":1,"date_published":"2016-07-20T00:00:00Z","conference":{"start_date":"2016-07-20","end_date":"2016-07-24","name":"GECCO: Genetic and evolutionary computation conference","location":"Denver, CO, USA"},"oa_version":"Published Version","abstract":[{"text":"Crossing fitness valleys is one of the major obstacles to function optimization. In this paper we investigate how the structure of the fitness valley, namely its depth d and length ℓ, influence the runtime of different strategies for crossing these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the Metropolis algorithm. While the (1+1) EA has to jump across the valley to a point of higher fitness because it does not accept decreasing moves, the non-elitist algorithms may cross the valley by accepting worsening moves. We show that while the runtime of the (1+1) EA algorithm depends critically on the length of the valley, the runtimes of the non-elitist algorithms depend crucially only on the depth of the valley. In particular, the expected runtime of both SSWM and Metropolis is polynomial in ℓ and exponential in d while the (1+1) EA is efficient only for valleys of small length. Moreover, we show that both SSWM and Metropolis can also efficiently optimize a rugged function consisting of consecutive valleys.","lang":"eng"}]}