On the Impact of the Cutoff Time on the Performance of Algorithm Configurators

04/12/2019
by   George T. Hall, et al.
0

Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search configurator (ParamRLS) for tuning the neighbourhood size k of the RLS_k algorithm. We measure performance as the expected number of configuration evaluations required to identify the optimal value for the parameter. We analyse the impact of the cutoff time κ (the time spent evaluating a configuration for a problem instance) on the expected number of configuration evaluations required to find the optimal parameter value, where we compare configurations using either best found fitness values (ParamRLS-F) or optimisation times (ParamRLS-T). We consider tuning RLS_k for a variant of the Ridge function class (Ridge*), where the performance of each parameter value does not change during the run, and for the OneMax function class, where longer runs favour smaller k. We rigorously prove that ParamRLS-F efficiently tunes RLS_k for Ridge* for any κ while ParamRLS-T requires at least a quadratic one. For OneMax ParamRLS-F identifies k=1 as optimal with linear κ while ParamRLS-T requires a κ of at least Ω(n n). For smaller κ ParamRLS-F identifies that k>1 performs better while ParamRLS-T returns k chosen uniformly at random.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/09/2020

Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators

Recently it has been proved that a simple algorithm configurator called ...
research
07/02/2018

LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration

We consider the problem of configuring general-purpose solvers to run ef...
research
08/25/2019

The Ridge Path Estimator for Linear Instrumental Variables

This paper presents the asymptotic behavior of a linear instrumental var...
research
03/12/2016

Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm

We consider a multi-neighborhood local search algorithm with a large num...
research
10/21/2015

GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...
research
12/09/2015

Partial Reinitialisation for Optimisers

Heuristic optimisers which search for an optimal configuration of variab...
research
05/03/2013

Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

The problem of parameterization is often central to the effective deploy...

Please sign up or login with your details

Forgot password? Click here to reset