37 citations · 140 across the 33 of their papers we have counts for
6 papers · 1 filter
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial
Dirk Sudholt
We analyse the performance of well-known evolutionary algorithms (1+1)EA and (1+)EA in the prior noise model, where in each fitness evaluation the search point is altered before…
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
Edgar Covantes Osuna, Wanru Gao, Frank Neumann +1
Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points. W…
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Phan Trung Hai Nguyen, Dirk Sudholt
Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid…
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
Edgar Covantes Osuna, Dirk Sudholt
Many real optimisation problems lead to multimodal domains and so require the identification of multiple optima. Niching methods have been developed to maintain the population dive…
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism
Edgar Covantes Osuna, Dirk Sudholt
Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources on…
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
Dirk Sudholt
Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This book chapter reviews runtim…