Runtime Analysis for the NSGA-II: Provable Speed-Ups From Crossover

Benjamin Doerr, Zhongdi Qu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Very recently, the first mathematical runtime analyses for the NSGA-II, the most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, this is the first time such an advantage of crossover is proven for the NSGA-II. Our arguments can be transferred to single-objective optimization. They then prove that crossover can speed up the (µ + 1) genetic algorithm in a different way and more pronounced than known before. Our experiments confirm the added value of crossover and show that the observed advantages are even larger than what our proofs can guarantee.

Original languageEnglish
Title of host publicationAAAI-23 Technical Tracks 10
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAAAI Press
Pages12399-12407
Number of pages9
ISBN (Electronic)9781577358800
DOIs
Publication statusPublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume37

Conference

Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States
CityWashington
Period7/02/2314/02/23

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