10 papers
Speeding Up the NSGA-II via Dynamic Population Sizes
Benjamin Doerr, Martin S. Krejca, Simon Wietheger
Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-off…
First Mathematical Runtime Analyses of Multi-Objective Evolutionary Algorithms for Multi-Valued Decision Variables
Mingfeng Li, Zheng Cheng, Weijie Zheng +1
Problems defined on binary decision spaces have been intensively studied in the theory of multi-objective evolutionary algorithms (MOEAs). In contrast, no mathematical runtime anal…
Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer
Benjamin Doerr, Martin S. Krejca, Milan StankoviÄ
Together with the NSGA-II, the SPEA2 is one of the most widely used domination-based multi-objective evolutionary algorithms. For both algorithms, the known runtime guarantees are…
Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Yasser Alghouass, Benjamin Doerr, Martin S. Krejca +1
Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (M…
Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
Simon Wietheger, Benjamin Doerr
Despite significant progress in the field of mathematical runtime analysis of multi-objective evolutionary algorithms (MOEAs), the performance of MOEAs on discrete many-objective p…
Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It
Denis Antipov, Benjamin Doerr
Randomized search heuristics (RSHs) are known to have a certain robustness to noise. Mathematical analyses trying to quantify rigorously how robust RSHs are to a noisy access to th…