11 citations · 41 across the 13 of their papers we have counts for
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Understanding the Co-evolutionary Solution Exchange of the MOEA/D: Already the Smallest Communication Radius Gives the Best-Possible Speed-Up
Benjamin Doerr, Moaad Elmoutassim, Martin S. Krejca +1
Besides domination-based algorithms such as the NSGA-II, the decomposition-based MOEA/D is one of the most popular and successful optimization heuristic for multi-objective problem…
A Dynamic Decomposition Strategy for the MOEA/D With Proven Performance Guarantees
Benjamin Doerr, Martin S. Krejca, Noé Weeks
The MOEA/D is one of the most successful multi-objective evolutionary algorithms. It operates by decomposing the optimization problem into single-objective subproblems, which are t…
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 Stanković
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…
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…
Tight Runtime Guarantees From Understanding the Population Dynamics of the GSEMO Multi-Objective Evolutionary Algorithm
Benjamin Doerr, Martin Krejca, Andre Opris
The global simple evolutionary multi-objective optimizer (GSEMO) is a simple, yet often effective multi-objective evolutionary algorithm (MOEA). By only maintaining non-dominated s…
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…
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
Marcel Chwiałkowski, Benjamin Doerr, Martin S. Krejca
The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) -- another si…
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…
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…
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…
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…
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise
Denis Antipov, Benjamin Doerr, Alexandra Ivanova
Experience shows that typical evolutionary algorithms can cope well with stochastic disturbances such as noisy function evaluations. In this first mathematical runtime analysis of…
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…
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…
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…