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20152026
most citedA Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes

22 citations · 84 across the 31 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.NE2024★ 4 cited

Speeding Up the NSGA-II With a Simple Tie-Breaking Rule

Benjamin Doerr, Tudor Ivan, Martin S. Krejca

The non-dominated sorting genetic algorithm~II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcom…

cs.NE2024★ 4 cited

Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces

Benjamin Doerr, Martin S. Krejca, Günter Rudolph

Randomized search heuristics have been applied successfully to a plethora of problems. This success is complemented by a large body of theoretical results. Unfortunately, the vast…

cs.NE2024★ 8 cited

Difficulties of the NSGA-II with the Many-Objective LeadingOnes Problem

Benjamin Doerr, Dimitri Korkotashvili, Martin S. Krejca

The NSGA-II is the most prominent multi-objective evolutionary algorithm (cited more than 50,000 times). Very recently, a mathematical runtime analysis has proven that this algorit…

cs.NE2024

Proven Runtime Guarantees for How the MOEA/D Computes the Pareto Front From the Subproblem Solutions

Benjamin Doerr, Martin S. Krejca, Noé Weeks

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) does not directly optimize a given multi-objective function , but instead optimizes single-object…

cs.NE2024★ 4 cited

Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics

Benjamin Doerr, Martin S. Krejca, Nguyen Vu

The target set selection problem (TSS) asks for a set of vertices such that an influence spreading process started in these vertices reaches the whole graph. The current state of t…

cs.NE2024★ 2 cited

A Flexible Evolutionary Algorithm With Dynamic Mutation Rate Archive

Martin S. Krejca, Carsten Witt

We propose a new, flexible approach for dynamically maintaining successful mutation rates in evolutionary algorithms using -bit flip mutations. The algorithm adds successful mut…