{"title":"When non-elitism outperforms elitism for crossing fitness valleys","project":[{"_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","grant_number":"618091","call_identifier":"FP7"}],"day":"20","file_date_updated":"2020-07-14T12:44:45Z","language":[{"iso":"eng"}],"license":"https://creativecommons.org/licenses/by/4.0/","citation":{"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.","ieee":"P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, and B. Trubenova, “When non-elitism outperforms elitism for crossing fitness valleys,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference 2016 </i>, Denver, CO, USA, 2016, pp. 1163–1170.","ama":"Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. When non-elitism outperforms elitism for crossing fitness valleys. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference 2016 </i>. ACM; 2016:1163-1170. doi:<a href=\"https://doi.org/10.1145/2908812.2908909\">10.1145/2908812.2908909</a>","chicago":"Oliveto, Pietro, Tiago Paixao, Jorge Heredia, Dirk Sudholt, and Barbora Trubenova. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference 2016 </i>, 1163–70. ACM, 2016. <a href=\"https://doi.org/10.1145/2908812.2908909\">https://doi.org/10.1145/2908812.2908909</a>.","mla":"Oliveto, Pietro, et al. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” <i>Proceedings of the Genetic and Evolutionary Computation Conference 2016 </i>, ACM, 2016, pp. 1163–70, doi:<a href=\"https://doi.org/10.1145/2908812.2908909\">10.1145/2908812.2908909</a>.","apa":"Oliveto, P., Paixao, T., Heredia, J., Sudholt, D., &#38; Trubenova, B. (2016). When non-elitism outperforms elitism for crossing fitness valleys. In <i>Proceedings of the Genetic and Evolutionary Computation Conference 2016 </i> (pp. 1163–1170). Denver, CO, USA: ACM. <a href=\"https://doi.org/10.1145/2908812.2908909\">https://doi.org/10.1145/2908812.2908909</a>","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."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","type":"conference","conference":{"name":"GECCO: Genetic and evolutionary computation conference","end_date":"2016-07-24","location":"Denver, CO, USA","start_date":"2016-07-20"},"status":"public","quality_controlled":"1","scopus_import":1,"tmp":{"short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"date_created":"2018-12-11T11:51:31Z","page":"1163 - 1170","oa":1,"publication_status":"published","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"publist_id":"5900","_id":"1349","ddc":["576"],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference 2016 ","oa_version":"Published Version","date_published":"2016-07-20T00:00:00Z","abstract":[{"lang":"eng","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."}],"ec_funded":1,"pubrep_id":"650","author":[{"full_name":"Oliveto, Pietro","first_name":"Pietro","last_name":"Oliveto"},{"id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","full_name":"Paixao, Tiago","first_name":"Tiago","last_name":"Paixao","orcid":"0000-0003-2361-3953"},{"last_name":"Heredia","first_name":"Jorge","full_name":"Heredia, Jorge"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"},{"id":"42302D54-F248-11E8-B48F-1D18A9856A87","full_name":"Trubenova, Barbora","last_name":"Trubenova","first_name":"Barbora","orcid":"0000-0002-6873-2967"}],"date_updated":"2021-01-12T06:50:03Z","publisher":"ACM","year":"2016","has_accepted_license":"1","file":[{"date_updated":"2020-07-14T12:44:45Z","file_size":979026,"file_name":"IST-2016-650-v1+1_p1163-oliveto.pdf","file_id":"5214","access_level":"open_access","checksum":"a1896e39e4113f2711e46b435d5f3e69","creator":"system","date_created":"2018-12-12T10:16:27Z","relation":"main_file","content_type":"application/pdf"}],"month":"07","doi":"10.1145/2908812.2908909"}