19 citations · 95 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential
Denis Antipov, Aneta Neumann, Frank Neumann
The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such…
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…
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov +1
Real-world optimization problems often involve stochastic and dynamic components. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to…
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
Denis Antipov, Aneta Neumann, Frank Neumann +1
Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems i…