• Publications
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Black-Box Search by Unbiased Variation
This paper introduces a more restricted black-box model for optimisation of pseudo-Boolean functions which it is claimed captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search, and others. Expand
A runtime analysis of simple hyper-heuristics: to mix or not to mix operators
This work analyzes the runtime of hyper-heuristics rigorously and shows that mixing heuristics could lead to exponentially faster search than individual (deterministically chosen) heuristic on chosen problems. Expand
Fitness-levels for non-elitist populations
  • P. Lehre
  • Mathematics, Computer Science
  • GECCO '11
  • 12 July 2011
An easy to use technique for deriving upper bounds on the expected runtime of non-elitist population-based evolutionary algorithms (EAs) and applications show how the efficiency of EAs is critically dependant on having a sufficiently strong selective pressure. Expand
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Boolean functions,Expand
Self-adaptation of Mutation Rates in Non-elitist Populations
Level-based analysis is applied to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates. Expand
Unbiased Black-Box Complexity of Parallel Search
A new black-box complexity model for search algorithms evaluating λ search points in parallel that captures the inertia caused by offspring populations in evolutionary algorithms and the total computational effort in parallel metaheuristics is proposed. Expand
Runtime analysis of selection hyper-heuristics with classical learning mechanisms
This paper derives the runtime of selection hyper-heuristic with a number of the most commonly used learning mechanisms not only on a classical example problem, but also on a general model of fitness landscapes, which helps in understanding the behaviour of hyper- heuristics. Expand
Negative Drift in Populations
  • P. Lehre
  • Computer Science
  • PPSN
  • 11 September 2010
A new drift theorem for populations is presented that allows properties of their long-term behaviour, e. Expand
General Drift Analysis with Tail Bounds
This work provides a general drift theorem that includes bounds on the upper and lower tail of the hitting time distribution and can be specialized into virtually all existing drift theorems with drift towards the target from the literature. Expand
Level-Based Analysis of Genetic Algorithms and Other Search Processes
The level-based theorem is presented, a new technique tailored to population-based processes where offspring are sampled independently from a distribution depending only on the current population that provides upper bounds on the expected time until the process reaches a target state. Expand