19 citations · 95 across the 21 of their papers we have counts for
21 papers
Parent Selection Mechanisms in Elitist Crossover-Based Algorithms
Andre Opris, Denis Antipov
Parent selection methods are widely used in evolutionary computation to accelerate the optimization process, yet their theoretical benefits are still poorly understood. In this pap…
When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
Denis Antipov, Carola Doerr
Online algorithm selection (OAS) aims to adapt the optimization process to changes in the fitness landscape and is expected to outperform any single algorithm from a given portfoli…
Enhancing Parameter Control Policies with State Information
Gianluca Covini, Denis Antipov, Carola Doerr
Parameter control and dynamic algorithm configuration study how to dynamically choose suitable configurations of a parametrized algorithm during the optimization process. Despite b…
Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems
Saba Sadeghi Ahouei, Denis Antipov, Aneta Neumann +1
Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a…
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…
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax
Denis Antipov, Aneta Neumann, Frank Neumann
The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness. In the context of multi-objective optimization th…
Larger Offspring Populations Help the Genetic Algorithm to Overcome the Noise
Alexandra Ivanova, Denis Antipov, Benjamin Doerr
Evolutionary algorithms are known to be robust to noise in the evaluation of the fitness. In particular, larger offspring population sizes often lead to strong robustness. We analy…
Coevolutionary Pareto Diversity Optimization
Aneta Neumann, Denis Antipov, Frank Neumann
Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years. In this paper, we introduce a coevolutionary Pareto…
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
Most evolutionary algorithms have multiple parameters and their values drastically affect the performance. Due to the often complicated interplay of the parameters, setting these v…
Runtime Analysis of a Heavy-Tailed Genetic Algorithm on Jump Functions
Denis Antipov, Benjamin Doerr
It was recently observed that the genetic algorithm can comparably easily escape the local optimum of the jump functions benchmark. Consequently, this algorithm can opt…
First Steps Towards a Runtime Analysis When Starting With a Good Solution
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
The mathematical runtime analysis of evolutionary algorithms traditionally regards the time an algorithm needs to find a solution of a certain quality when initialized with a rando…
Fixed-Target Runtime Analysis
Maxim Buzdalov, Benjamin Doerr, Carola Doerr +1
Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization probl…
A Rigorous Runtime Analysis of the GA on Jump Functions
Denis Antipov, Benjamin Doerr, Vitalii Karavaev
The genetic algorithm is a younger evolutionary algorithm trying to profit also from inferior solutions. Rigorous runtime analyses on unimodal fitness functions showe…
Fast Mutation in Crossover-based Algorithms
Denis Antipov, Maxim Buzdalov, Benjamin Doerr
The heavy-tailed mutation operator proposed in Doerr, Le, Makhmara, and Nguyen (GECCO 2017), called \emph{fast mutation} to agree with the previously used language, so far was prov…
The Efficiency Threshold for the Offspring Population Size of the (, ) EA
Denis Antipov, Benjamin Doerr, Quentin Yang
Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this…
A Tight Runtime Analysis for the EA
Denis Antipov, Benjamin Doerr
Despite significant progress in the theory of evolutionary algorithms, the theoretical understanding of evolutionary algorithms which use non-trivial populations remains challengin…
Precise Runtime Analysis for Plateau Functions
Denis Antipov, Benjamin Doerr
To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the -dimensional Plateau function as natur…