no code implementations • 17 Apr 2024 • Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice.
no code implementations • 7 Jun 2023 • Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt
To our knowledge, this is the first proof that NSGA-II can outperform GSEMO and the first runtime analysis of NSGA-II in noisy optimisation.
no code implementations • 31 Jan 2023 • Duc-Cuong Dang, Andre Opris, Dirk Sudholt
We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of "royal road" functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used.
no code implementations • 23 Aug 2019 • Duc-Cuong Dang, Anton Eremeev, Per Kristian Lehre
In contrast to this negative result, we also show that for any linear function with polynomially bounded weights, the EA achieves a polynomial expected runtime if the mutation rate is reduced to $\Theta(1/n^2)$ and the population size is sufficiently large.
no code implementations • 26 Jul 2018 • Duc-Cuong Dang, Per Kristian Lehre, Phan Trung Hai Nguyen
The facility and generality of our arguments suggest that this is a promising approach to derive bounds on the expected optimisation time of EDAs.
no code implementations • 10 Aug 2016 • Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton
This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared to (1+1) EA, which requires time $\Theta(n^k)$.
no code implementations • 12 Jul 2016 • Duc-Cuong Dang, Thomas Jansen, Per Kristian Lehre
It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future.
no code implementations • 17 Jun 2016 • Duc-Cuong Dang, Per Kristian Lehre
Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation.
no code implementations • 7 Dec 2015 • Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time.
no code implementations • 29 Jul 2014 • Dogan Corus, Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre
Finally, we prove that the theorem is nearly optimal for the processes considered.