{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T14:29:49Z","timestamp":1759847389494,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Different from single-objective evolutionary algorithms, where non-elitism is an established concept, multi-objective evolutionary algorithms almost always select the next population in a greedy fashion. In the only notable exception, a stochastic selection mechanism was recently proposed for the SMS-EMOA and was proven to speed up computing the Pareto front of the bi-objective jump benchmark with problem size n and gap parameter k by a factor of max{1,2^(k\/4)\/n}. While this constitutes the first proven speed-up from non-elitist selection, suggesting a very interesting research direction, it has to be noted that a true speed-up only occurs for k \u2265 4log(n), where the runtime is super-polynomial, and that the advantage reduces for larger numbers of objectives as shown in a later work. In this work, we propose a different non-elitist selection mechanism based on aging, which exempts individuals younger than a certain age from a possible removal. This remedies the two shortcomings of  stochastic selection: We prove a speed-up by a factor of max{1,\u0398(k)^(k-1)}, regardless of the number of objectives. In particular,  a  positive speed-up can already be observed for constant k, the only setting for which polynomial runtimes can be witnessed. Overall, this result supports the use of non-elitist selection schemes, but suggests that aging-based mechanisms can be considerably more powerful than stochastic selection mechanisms.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/988","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"8885-8893","source":"Crossref","is-referenced-by-count":1,"title":["Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy"],"prefix":"10.24963","author":[{"given":"Mingfeng","family":"Li","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Doerr","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:45Z","timestamp":1758627345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/988"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/988","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}