most citedGreedy Routing and the Algorithmic Small-World Phenomenom

5 citations · 5 across the 1 of their papers we have counts for

collaborators

8 papers

cs.NE2024

Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler

Diederick Vermetten, Johannes Lengler, Dimitri Rusin +2

Optimization problems in dynamic environments have recently been the source of several theoretical studies. One of these problems is the monotonic Dynamic Binary Value problem, whi…

cs.NE2024

How Population Diversity Influences the Efficiency of Crossover

Sacha Cerf, Johannes Lengler

Our theoretical understanding of crossover is limited by our ability to analyze how population diversity evolves. In this study, we provide one of the first rigorous analyses of po…

cs.NE2024

Faster Optimization Through Genetic Drift

Cella Florescu, Marc Kaufmann, Johannes Lengler +1

The compact Genetic Algorithm (cGA), parameterized by its hypothetical population size , offers a low-memory alternative to evolving a large offspring population of solutions. I…

cs.NE2024

Self-Adjusting Evolutionary Algorithms Are Slow on Multimodal Landscapes

Johannes Lengler, Konstantin Sturm

The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains. They have also been transferred to control the offspring population size…

cs.NE20242 cited

Plus Strategies are Exponentially Slower for Planted Optima of Random Height

Johannes Lengler, Leon Schiller, Oliver Sieberling

We compare the -EA and the -EA on the recently introduced benchmark DisOM, which is the OneMax function with randomly planted local optima. Previous work showed tha…

cs.NE2023

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…