Paper Info
Title
Designing and Comparing Multiple Portfolios of Parameter Configurations for Online Algorithm Selection.
Abstract
Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We apply our approach on a Simulated Annealing-Tabu Search (SA-TS) hybrid algorithm for solving the Quadratic Assignment Problem (QAP) as well as an Iterated Local Search (ILS) on the Travelling Salesman Problem (TSP). We also generate a portfolio of parameter configurations using best-ofbreed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.
Year
DOI
Venue
2016
10.1007/978-3-319-50349-3_7
Lecture Notes in Computer Science
Field
DocType
Volume
Online algorithm,Mathematical optimization,Hybrid algorithm,Computer science,Quadratic assignment problem,Portfolio,Travelling salesman problem,Artificial intelligence,Algorithm Selection,Machine learning,Iterated local search,Design of experiments
Conference
10079
ISSN
Citations 
PageRank 
0302-9743
1
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Aldy Gunawan114212.18
Hoong Chuin Lau273991.69
Mustafa Misir351.76