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

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 (also at AAAI 2023), 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. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the work Benjamin Doerr, Zhongdi Qu. Runtime analysis for the NSGA-II: Provable speed-ups from crossover, Conference on Artificial Intelligence, AAAI 2023. AAAI Press, to appear. [13].