An evaluation of machine learning in algorithm selection for search problems

2012 ◽  
Vol 25 (3) ◽  
pp. 257-270 ◽  
Author(s):  
Lars Kotthoff ◽  
Ian P. Gent ◽  
Ian Miguel
Keyword(s):  
Machine Learning ◽  
Search Problems ◽  
Algorithm Selection ◽  
Selection For

scholarly journals Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 19
Author(s):  
Mario Andrés Muñoz ◽  
Michael Kirley
Keyword(s):  
Machine Learning ◽  
Random Sampling ◽  
Black Box ◽  
Landscape Analysis ◽  
Algorithm Selection ◽  
Validation Methods ◽  
Automated Algorithm ◽  
Trade Offs ◽  
Selection For ◽  
Single Objective

In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs.

Sign in / Sign up

Export Citation Format

Share Document