37 citations · 140 across the 33 of their papers we have counts for
4 papers · 1 filter
Memetic Algorithms: Parametrization and Balancing Local and Global Search
Dirk Sudholt
This is a preprint of a book chapter from the Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, ISBN 978-3-642-23246-6, Springer, edited by F. Neri,…
Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization
Jörg Lässig, Dirk Sudholt
Evolutionary algorithms are popular heuristics for solving various combinatorial problems as they are easy to apply and often produce good results. Island models parallelize evolut…
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
Dirk Sudholt
We present a new method for proving lower bounds on the expected running time of evolutionary algorithms. It is based on fitness-level partitions and an additional condition on tra…
Adaptive Population Models for Offspring Populations and Parallel Evolutionary Algorithms
Jörg Lässig, Dirk Sudholt
We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population…