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

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