MMAS vs. population-based EA on a family of dynamic fitness functions. - Andrei Lissovoi; Carsten Witt (GECCO, 2014)

(1+1) EA on Generalized Dynamic OneMax. - Timo Kötzing; Andrei Lissovoi; Carsten Witt (FOGA, 2015)

On the Utility of Island Models in Dynamic Optimization. - Andrei Lissovoi; Carsten Witt (GECCO, 2015)

Runtime analysis of ant colony optimization on dynamic shortest path problems. - Andrei Lissovoi; Carsten Witt (Theor. Comput. Sci., 2015)

The Impact of Migration Topology on the Runtime of Island Models in Dynamic Optimization. - Andrei Lissovoi; Carsten Witt (GECCO, 2016)

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions. - Andrei Lissovoi; Carsten Witt (Algorithmica, 2016)

Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems. - Andrei Lissovoi (2016)

On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (GECCO, 2017)

Theoretical results on bet-and-run as an initialisation strategy. - Andrei Lissovoi; Dirk Sudholt; Markus Wagner 007; Christine Zarges (GECCO, 2017)

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization. - Andrei Lissovoi; Carsten Witt (Algorithmica, 2017)

On the runtime analysis of selection hyper-heuristics with adaptive learning periods. - Benjamin Doerr; Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (GECCO, 2018)

Tutorials at PPSN 2018. - Gisele Lobo Pappa; Michael T. M. Emmerich; Ana L. C. Bazzan; Will N. Browne; Kalyanmoy Deb; Carola Doerr; Marko Durasevic; Michael G. Epitropakis; Saemundur O. Haraldsson; Domagoj Jakobovic; Pascal Kerschke; Krzysztof Krawiec; et al. (PPSN (2), 2018)

The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization. - Andrei Lissovoi; Carsten Witt (Algorithmica, 2018)

Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean Optimisation. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (CoRR, 2018)

Computational Complexity Analysis of Genetic Programming. - Andrei Lissovoi; Pietro S. Oliveto (CoRR, 2018)

On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (AAAI, 2019)

Evolving boolean functions with conjunctions and disjunctions via genetic programming. - Benjamin Doerr; Andrei Lissovoi; Pietro S. Oliveto (GECCO, 2019)

On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. - Andrei Lissovoi; Pietro S. Oliveto (J. Artif. Intell. Res., 2019)

How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (AAAI, 2020)

Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes*. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (Evol. Comput., 2020)

Computational Complexity Analysis of Genetic Programming. - Andrei Lissovoi; Pietro S. Oliveto (Theory of Evolutionary Computation, 2020)

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best. - Dogan Corus; Andrei Lissovoi; Pietro S. Oliveto; Carsten Witt (ACM Trans. Evol. Learn. Optim., 2021)

When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not. - Andrei Lissovoi; Pietro S. Oliveto; John Alasdair Warwicker (Artif. Intell., 2023)

(1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error. - Benjamin Doerr; Andrei Lissovoi; Pietro S. Oliveto (Artif. Intell., 2023)