Improved Fixed-Budget Results via Drift Analysis

06/12/2020
by   Timo Kötzing, et al.
0

Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations. Despite recent progress in fixed-budget theory, there is a lack of general tools to derive such results. We transfer drift theory, the key tool to derive expected optimization times, to the fixed-budged perspective. A first and easy-to-use statement concerned with iterating drift in so-called greed-admitting scenarios immediately translates into bounds on the expected function value. Afterwards, we consider a more general tool based on the well-known variable drift theorem. Applications of this technique to the LeadingOnes benchmark function yield statements that are more precise than the previous state of the art.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/09/2013

General Drift Analysis with Tail Bounds

Drift analysis is one of the state-of-the-art techniques for the runtime...
research
09/07/2019

Unlimited Budget Analysis of Randomised Search Heuristics

Performance analysis of all kinds of randomised search heuristics is a r...
research
05/22/2018

Intuitive Analyses via Drift Theory

Humans are bad with probabilities, and the analysis of randomized algori...
research
05/22/2018

First-Hitting Times Under Additive Drift

For the last ten years, almost every theoretical result concerning the e...
research
02/09/2018

Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule

This paper explores the use of the standard approach for proving runtime...
research
04/16/2019

Maximizing Drift is Not Optimal for Solving OneMax

It seems very intuitive that for the maximization of the OneMax problem ...
research
07/17/2023

Vocoder drift compensation by x-vector alignment in speaker anonymisation

For the most popular x-vector-based approaches to speaker anonymisation,...

Please sign up or login with your details

Forgot password? Click here to reset