37 citations · 140 across the 33 of their papers we have counts for
35 papers
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis
Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Runtime analysis has recently been applied to popular evolutionary multi-objective (EMO) algorithms like NSGA-II in order to establish a rigorous theoretical foundation. However, m…
Runtime Analyses of NSGA-III on Many-Objective Problems
Andre Opris, Duc-Cuong Dang, Frank Neumann +1
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is des…
Achieving Tight Runtime Bounds on Jump by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
Andre Opris, Johannes Lengler, Dirk Sudholt
The JUMP benchmark was the first problem for which crossover was proven to give a speed-up over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper…
Analysing the Robustness of NSGA-II under Noise
Duc-Cuong Dang, Andre Opris, Bahare Salehi +1
Runtime analysis has produced many results on the efficiency of simple evolutionary algorithms like the (1+1) EA, and its analogue called GSEMO in evolutionary multiobjective optim…
Runtime Analysis of Quality Diversity Algorithms
Jakob Bossek, Dirk Sudholt
Quality diversity~(QD) is a branch of evolutionary computation that gained increasing interest in recent years. The Map-Elites QD approach defines a feature space, i.e., a partitio…
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima
Joost Jorritsma, Johannes Lengler, Dirk Sudholt
It is an ongoing debate whether and how comma selection in evolutionary algorithms helps to escape local optima. We propose a new benchmark function to investigate the benefits of…