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ORCIDAndrei Lissovoi
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2020 – today
- 2023
[j9]Andrei Lissovoi, Pietro S. Oliveto
, John Alasdair Warwicker:
When move acceptance selection hyper-heuristics outperform Metropolis and elitist evolutionary algorithms and when not. Artif. Intell. 314: 103804 (2023)- [j8]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error. Artif. Intell. 319: 103906 (2023) - [i5]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error. CoRR abs/2303.07455 (2023) - 2021
- [j7]Dogan Corus, Andrei Lissovoi, Pietro S. Oliveto, Carsten Witt:
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best. ACM Trans. Evol. Learn. Optim. 1(1): 2:1-2:38 (2021) - [i4]Dogan Corus, Andrei Lissovoi, Pietro S. Oliveto, Carsten Witt:
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best. CoRR abs/2103.10394 (2021) - 2020
- [j6]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes*. Evol. Comput. 28(3): 437-461 (2020) - [c13]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics. AAAI 2020: 2376-2383 - [p1]Andrei Lissovoi, Pietro S. Oliveto:
Computational Complexity Analysis of Genetic Programming. Theory of Evolutionary Computation 2020: 475-518
2010 – 2019
- 2019
- [j5]Andrei Lissovoi, Pietro S. Oliveto:
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. J. Artif. Intell. Res. 66: 655-689 (2019) - [c12]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation. AAAI 2019: 2322-2329 - [c11]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
Evolving boolean functions with conjunctions and disjunctions via genetic programming. GECCO 2019: 1003-1011 - [i3]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming. CoRR abs/1903.11936 (2019) - 2018
- [j4]Andrei Lissovoi, Carsten Witt:
The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization. Algorithmica 80(5): 1634-1657 (2018) - [c10]Andrei Lissovoi, Pietro S. Oliveto:
On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions. AAAI 2018: 1363-1370 - [c9]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the runtime analysis of selection hyper-heuristics with adaptive learning periods. GECCO 2018: 1015-1022 - [c8]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, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang:
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [i2]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean Optimisation. CoRR abs/1801.07546 (2018) - [i1]Andrei Lissovoi, Pietro S. Oliveto:
Computational Complexity Analysis of Genetic Programming. CoRR abs/1811.04465 (2018) - 2017
- [j3]Andrei Lissovoi, Carsten Witt:
A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization. Algorithmica 78(2): 641-659 (2017) - [c7]Andrei Lissovoi, Pietro S. Oliveto, John Alasdair Warwicker:
On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation. GECCO 2017: 849-856 - [c6]Andrei Lissovoi, Dirk Sudholt, Markus Wagner, Christine Zarges:
Theoretical results on bet-and-run as an initialisation strategy. GECCO 2017: 857-864 - 2016
- [b1]Andrei Lissovoi:
Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems. Technical University of Denmark, 2016 - [j2]Andrei Lissovoi, Carsten Witt:
MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions. Algorithmica 75(3): 554-576 (2016) - [c5]Andrei Lissovoi, Carsten Witt:
The Impact of Migration Topology on the Runtime of Island Models in Dynamic Optimization. GECCO 2016: 1155-1162 - 2015
- [j1]Andrei Lissovoi, Carsten Witt:
Runtime analysis of ant colony optimization on dynamic shortest path problems. Theor. Comput. Sci. 561: 73-85 (2015) - [c4]Timo Kötzing, Andrei Lissovoi, Carsten Witt:
(1+1) EA on Generalized Dynamic OneMax. FOGA 2015: 40-51 - [c3]Andrei Lissovoi, Carsten Witt:
On the Utility of Island Models in Dynamic Optimization. GECCO 2015: 1447-1454 - 2014
- [c2]Andrei Lissovoi, Carsten Witt:
MMAS vs. population-based EA on a family of dynamic fitness functions. GECCO 2014: 1399-1406 - 2013
- [c1]Andrei Lissovoi, Carsten Witt:
Runtime analysis of ant colony optimization on dynamic shortest path problems. GECCO 2013: 1605-1612
Coauthor Index
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