Runtime Analysis of (1+1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on OneMax+ZeroMax Problem. - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (EvoCOP, 2015)

Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives. - Denis Antipov; Arina Buzdalova (CEC, 2017)

Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learning. - Denis Antipov; Arina Buzdalova; Andrew Stankevich (GECCO (Companion), 2018)

A tight runtime analysis for the (μ + λ) EA. - Denis Antipov; Benjamin Doerr; Jiefeng Fang; Tangi Hetet (GECCO, 2018)

Precise Runtime Analysis for Plateaus. - Denis Antipov; Benjamin Doerr (PPSN (2), 2018)

A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones. - Denis Antipov; Benjamin Doerr; Vitalii Karavaev (FOGA, 2019)

The efficiency threshold for the offspring population size of the (µ, λ) EA. - Denis Antipov; Benjamin Doerr; Quentin Yang (GECCO, 2019)

Theoretical and empirical study of the (1 + (λ, λ)) EA on the leadingones problem. - Vitalii Karavaev; Denis Antipov; Benjamin Doerr (GECCO (Companion), 2019)

The Efficiency Threshold for the Offspring Population Size of the (μ, λ) EA. - Denis Antipov; Benjamin Doerr; Quentin Yang (CoRR, 2019)

The (1 + (λ, λ)) GA is even faster on multimodal problems. - Denis Antipov; Benjamin Doerr; Vitalii Karavaev (GECCO, 2020)

First Steps Towards a Runtime Analysis When Starting with a Good Solution. - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (PPSN (2), 2020)

Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions. - Denis Antipov; Benjamin Doerr (PPSN (2), 2020)

The (1 + (λ, λ)) GA Is Even Faster on Multimodal Problems. - Denis Antipov; Benjamin Doerr; Vitalii Karavaev (CoRR, 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 (2020)

The effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions. - Denis Antipov; Semen Naumov (FOGA, 2021)

The lower bounds on the runtime of the (1 + (λ, λ)) GA on the minimum spanning tree problem. - Matvey Shnytkin; Denis Antipov (GECCO Companion, 2021)

A Tight Runtime Analysis for the (μ + λ ) EA. - Denis Antipov; Benjamin Doerr (Algorithmica, 2021)

Precise Runtime Analysis for Plateau Functions. - Denis Antipov; Benjamin Doerr (ACM Trans. Evol. Learn. Optim., 2021)

Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022). - Denis Antipov; Benjamin Doerr (GECCO Companion, 2022)

Coevolutionary Pareto diversity optimization. - Aneta Neumann; Denis Antipov; Frank Neumann 001 (GECCO, 2022)

Fast Mutation in Crossover-Based Algorithms. - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (Algorithmica, 2022)

A Rigorous Runtime Analysis of the (1 + (λ , λ )) GA on Jump Functions. - Denis Antipov; Benjamin Doerr; Vitalii Karavaev (Algorithmica, 2022)

Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. - Denis Antipov; Aneta Neumann; Frank Neumann 001 (FOGA, 2023)

Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise. - Alexandra Ivanova; Denis Antipov; Benjamin Doerr (GECCO, 2023)

Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise. - Alexandra Ivanova; Denis Antipov; Benjamin Doerr (CoRR, 2023)

Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. - Denis Antipov; Benjamin Doerr; Alexandra Ivanova (GECCO, 2024)

A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. - Denis Antipov; Aneta Neumann; Frank Neumann 001 (GECCO, 2024)

Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. - Ishara Hewa Pathiranage; Frank Neumann 001; Denis Antipov; Aneta Neumann (GECCO, 2024)

Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. - Ishara Hewa Pathiranage; Frank Neumann 001; Denis Antipov; Aneta Neumann (GECCO, 2024)

Greedy Versus Curious Parent Selection for Multi-objective Evolutionary Algorithms. - Denis Antipov; Timo Kötzing; Aishwarya Radhakrishnan (PPSN (3), 2024)

Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. - Denis Antipov; Aneta Neumann; Frank Neumann 001 (PPSN (3), 2024)

Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem. - Denis Antipov; Aneta Neumann; Frank Neumann 001; Andrew M. Sutton (PPSN (3), 2024)

Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. - Denis Antipov; Aneta Neumann; Frank Neumann 001; Andrew M. Sutton (CoRR, 2024)

Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It. - Denis Antipov; Benjamin Doerr (CoRR, 2024)

Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (Algorithmica, 2024)

Code and data for "First Steps Towards a Runtime Analysis When Starting With a Good Solution". - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (Zenodo, 2024)

Feature-Based Evolutionary Diversity Optimization of Discriminating Instances for Chance-Constrained Optimization Problems. - Saba Sadeghi Ahouei; Denis Antipov; Aneta Neumann; Frank Neumann 001 (EvoCOP@EvoStar, 2025)

First Steps Toward a Runtime Analysis When Starting With a Good Solution. - Denis Antipov; Maxim Buzdalov 001; Benjamin Doerr (ACM Trans. Evol. Learn. Optim., 2025)

Enhancing Parameter Control Policies with State Information. - Gianluca Covini; Denis Antipov; Carola Doerr (CoRR, 2025)