Andrei Lissovoi

发表

On the Time and Space Complexity of Genetic Programming for Evolving Boolean Conjunctions

Pietro Simone Oliveto, Andrei Lissovoi, Andrei Lissovoi, 2018, AAAI.

The Impact of Migration Topology on the Runtime of Island Models in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, 2016, GECCO.

Hyper-heuristics Can Achieve Optimal Performance for Pseudo-Boolean Optimisation pdf

Pietro Simone Oliveto, Andrei Lissovoi, John Alasdair Warwicker, 2018, ArXiv.

Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems

Carsten Witt, Andrei Lissovoi, Andrei Lissovoi, 2016 .

On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation

Andrei Lissovoi, John Alasdair Warwicker, Pietro S. Oliveto, 2019, AAAI.

Simple Hyper-heuristics Optimise LeadingOnes in the Best Runtime Achievable Using Randomised Local Search Low-Level Heuristics

Andrei Lissovoi, John Alasdair Warwicker, Pietro S. Oliveto, 2018 .

Evolving boolean functions with conjunctions and disjunctions via genetic programming

Pietro Simone Oliveto, Benjamin Doerr, Andrei Lissovoi, 2019, GECCO.

Runtime analysis of ant colony optimization on dynamic shortest path problems

Carsten Witt, Andrei Lissovoi, C. Witt, 2013, GECCO '13.

MMAS vs. population-based EA on a family of dynamic fitness functions

Carsten Witt, Andrei Lissovoi, 2014, GECCO.

The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2017, Algorithmica.

On the runtime analysis of generalised selection hyper-heuristics for pseudo-boolean optimisation

Pietro Simone Oliveto, Andrei Lissovoi, John Alasdair Warwicker, 2017, GECCO.

Tutorials at PPSN 2018

Roman Senkerik, Per Kristian Lehre, Pietro Simone Oliveto, 2018, PPSN.

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2016, Algorithmica.

How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics

Andrei Lissovoi, John Alasdair Warwicker, Pietro S. Oliveto, 2020, AAAI.

(1+1) EA on Generalized Dynamic OneMax

Carsten Witt, Timo Kötzing, Andrei Lissovoi, 2015, FOGA.

Theoretical results on bet-and-run as an initialisation strategy

Dirk Sudholt, Christine Zarges, Markus Wagner, 2017, GECCO.

Analysis of Ant Colony Optimization for Dynamic Shortest Path Problems

Andrei Lissovoi, 2012 .

On the Utility of Island Models in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2015, GECCO.

Runtime analysis of ant colony optimization on dynamic shortest path problems

Carsten Witt, Andrei Lissovoi, 2015, Theor. Comput. Sci..

Computational Complexity Analysis of Genetic Programming pdf

Pietro Simone Oliveto, Andrei Lissovoi, Andrei Lissovoi, 2018, Theory of Evolutionary Computation.

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

Carsten Witt, Andrei Lissovoi, C. Witt, 2014, Algorithmica.

Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes*

Andrei Lissovoi, John Alasdair Warwicker, Pietro S. Oliveto, 2018, Evolutionary Computation.

On the runtime analysis of selection hyper-heuristics with adaptive learning periods

Pietro Simone Oliveto, Benjamin Doerr, Andrei Lissovoi, 2018, GECCO.

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best

Dogan Corus, Carsten Witt, Andrei Lissovoi, 2021, ACM Trans. Evol. Learn. Optim..