Denis Antipov

According to our database, Denis Antipov authored at least 35 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution.

Denis Antipov, Maxim Buzdalov, Benjamin Doerr

Algorithmica, February, 2024

Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It.

Denis Antipov, Benjamin Doerr

CoRR, 2024

Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem.

Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton

CoRR, 2024

Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem.

Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential.

Denis Antipov, Aneta Neumann, Frank Neumann

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms.

Denis Antipov, Timo Kötzing, Aishwarya Radhakrishnan

Proceedings of the Parallel Problem Solving from Nature - PPSN XVIII, 2024

Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem.

Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem.

Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax.

Denis Antipov, Aneta Neumann, Frank Neumann

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

Already Moderate Population Sizes Provably Yield Strong Robustness to Noise.

Denis Antipov, Benjamin Doerr, Alexandra Ivanova

Proceedings of the Genetic and Evolutionary Computation Conference, 2024

2023

Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise.

Alexandra Ivanova, Denis Antipov, Benjamin Doerr

CoRR, 2023

Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise.

Alexandra Ivanova, Denis Antipov, Benjamin Doerr

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax.

Denis Antipov, Aneta Neumann, Frank Neumann

Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023

2022

A Rigorous Runtime Analysis of the (1 + (λ , λ )) GA on Jump Functions.

Denis Antipov, Benjamin Doerr, Vitalii Karavaev

Algorithmica, 2022

Fast Mutation in Crossover-Based Algorithms.

Denis Antipov, Maxim Buzdalov, Benjamin Doerr

Algorithmica, 2022

Coevolutionary Pareto diversity optimization.

Aneta Neumann, Denis Antipov, Frank Neumann

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022).

Denis Antipov, Benjamin Doerr

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021

Precise Runtime Analysis for Plateau Functions.

Denis Antipov, Benjamin Doerr

ACM Trans. Evol. Learn. Optim., 2021

A Tight Runtime Analysis for the (μ + λ ) EA.

Denis Antipov, Benjamin Doerr

Algorithmica, 2021

The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem.

Matvey Shnytkin, Denis Antipov

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions.

Denis Antipov, Semen Naumov

Proceedings of the FOGA '21: Foundations of Genetic Algorithms XVI, 2021

2020

Methods for Ti­ght Analysis of Popu­lation-based Evolutionary Algorithms. (Méthodes d'analyse précise des algorithmes évolutifs basés sur la population / Методы точного анализа популяционных эволюционных алгоритмов).

Denis Antipov

PhD thesis, 2020

The (1 + (λ, λ)) GA Is Even Faster on Multimodal Problems.

Denis Antipov, Benjamin Doerr, Vitalii Karavaev

CoRR, 2020

Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions.

Denis Antipov, Benjamin Doerr

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

First Steps Towards a Runtime Analysis When Starting with a Good Solution.

Denis Antipov, Maxim Buzdalov, Benjamin Doerr

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

The (1 + (<i>λ, λ</i>)) GA is even faster on multimodal problems.

Denis Antipov, Benjamin Doerr, Vitalii Karavaev

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

The Efficiency Threshold for the Offspring Population Size of the (μ, λ) EA.

Denis Antipov, Benjamin Doerr, Quentin Yang

CoRR, 2019

Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem.

Vitalii Karavaev, Denis Antipov, Benjamin Doerr

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

The efficiency threshold for the offspring population size of the (<i>µ, λ</i>) EA.

Denis Antipov, Benjamin Doerr, Quentin Yang

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones.

Denis Antipov, Benjamin Doerr, Vitalii Karavaev

Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019

2018

Precise Runtime Analysis for Plateaus.

Denis Antipov, Benjamin Doerr

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

A tight runtime analysis for the (μ + λ) EA.

Denis Antipov, Benjamin Doerr, Jiefeng Fang, Tangi Hetet

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning.

Denis Antipov, Arina Buzdalova, Andrew Stankevich

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

2017

Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives.

Denis Antipov, Arina Buzdalova

Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

2015

Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem.

Denis Antipov, Maxim Buzdalov, Benjamin Doerr

Proceedings of the Evolutionary Computation in Combinatorial Optimization, 2015