Runtime analysis of ant colony optimization on dynamic shortest path problems A Lissovoi, C Witt Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 63 | 2013 |
(1+ 1) EA on generalized dynamic OneMax T Kötzing, A Lissovoi, C Witt Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms …, 2015 | 50 | 2015 |
On the runtime analysis of selection hyper-heuristics with adaptive learning periods B Doerr, A Lissovoi, PS Oliveto, JA Warwicker Proceedings of the Genetic and Evolutionary Computation Conference, 1015-1022, 2018 | 49 | 2018 |
On the time complexity of algorithm selection hyper-heuristics for multimodal optimisation A Lissovoi, PS Oliveto, JA Warwicker Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2322-2329, 2019 | 47 | 2019 |
MMAS vs. population-based EA on a family of dynamic fitness functions A Lissovoi, C Witt Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 36 | 2014 |
On the runtime analysis of generalised selection hyper-heuristics for pseudo-Boolean optimisation A Lissovoi, PS Oliveto, JA Warwicker Proceedings of the Genetic and Evolutionary Computation Conference, 849-856, 2017 | 35 | 2017 |
Simple hyper-heuristics control the neighbourhood size of randomised local search optimally for LeadingOnes A Lissovoi, PS Oliveto, JA Warwicker Evolutionary Computation 28 (3), 437-461, 2020 | 32 | 2020 |
A runtime analysis of parallel evolutionary algorithms in dynamic optimization A Lissovoi, C Witt Algorithmica 78, 641-659, 2017 | 30 | 2017 |
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions A Lissovoi, PS Oliveto Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence …, 2018 | 21 | 2018 |
On steady-state evolutionary algorithms and selective pressure: Why inverse rank-based allocation of reproductive trials is best D Corus, A Lissovoi, PS Oliveto, C Witt ACM Transactions on Evolutionary Learning and Optimization 1 (1), 1-38, 2021 | 16 | 2021 |
How the duration of the learning period affects the performance of random gradient selection hyper-heuristics A Lissovoi, P Oliveto, JA Warwicker Proceedings of the AAAI Conference on Artificial Intelligence 34 (03), 2376-2383, 2020 | 15 | 2020 |
When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not A Lissovoi, PS Oliveto, JA Warwicker Artificial Intelligence 314, 103804, 2023 | 11 | 2023 |
Computational complexity analysis of genetic programming A Lissovoi, PS Oliveto Theory of Evolutionary Computation: Recent Developments in Discrete …, 2020 | 11 | 2020 |
Evolving boolean functions with conjunctions and disjunctions via genetic programming B Doerr, A Lissovoi, PS Oliveto Proceedings of the Genetic and Evolutionary Computation Conference, 1003-1011, 2019 | 11 | 2019 |
The impact of a sparse migration topology on the runtime of island models in dynamic optimization A Lissovoi, C Witt Algorithmica 80 (5), 1634-1657, 2018 | 8 | 2018 |
Theoretical results on bet-and-run as an initialisation strategy A Lissovoi, D Sudholt, M Wagner, C Zarges Proceedings of the Genetic and Evolutionary Computation Conference, 857-864, 2017 | 8 | 2017 |
On the utility of island models in dynamic optimization A Lissovoi, C Witt Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 7 | 2015 |
The impact of migration topology on the runtime of island models in dynamic optimization A Lissovoi, C Witt Proceedings of the Genetic and Evolutionary Computation Conference 2016 …, 2016 | 3 | 2016 |
Hyper-heuristics can achieve optimal performance for pseudo-boolean optimisation A Lissovoi, PS Oliveto, JA Warwicker Arxiv e-prints 2018, 1801 | 3 | 1801 |
Simple hyper-heuristics optimise leadingones in the best runtime achievable using randomised local search low-level heuristics A Lissovoi, PS Oliveto, JA Warwicker ArXiv e-prints, 2018 | 2 | 2018 |