Abstract
We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities between fitness levels. The method yields exact or near-exact lower bounds for LO, OneMax, and all functions with a unique optimum. All lower bounds hold for every evolutionary algorithm that only uses standard mutation as variation operator. © 2010 Springer-Verlag.
Cite
CITATION STYLE
Sudholt, D. (2010). General lower bounds for the running time of evolutionary algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6238 LNCS, pp. 124–133). https://doi.org/10.1007/978-3-642-15844-5_13
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
