Paper Info
Title
OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms
Abstract
This paper introduces an automated approach called OSCAR that combines algorithm portfolios and online algorithm selection. The goal of algorithm portfolios is to construct a subset of algorithms with diverse problem solving capabilities. The portfolio is then used to select algorithms from for solving a particular (set of) instance(s). Traditionally, algorithm selection is usually performed in an offline manner and requires the need of domain knowledge about the target problem; while online algorithm selection techniques tend not to pay much attention to a careful construction of algorithm portfolios. By combining algorithm portfolios and online selection, our hope is to design a problem-independent hybrid strategy with diverse problem solving capability. We apply OSCAR to design a portfolio of memetic operator combinations, each including one crossover, one mutation and one local search rather than single operator selection. An empirical analysis is performed on the Quadratic Assignment and Flowshop Scheduling problems to verify the feasibility, efficacy, and robustness of our proposed approach.
Year
DOI
Venue
2015
10.1007/978-3-319-19084-6_6
Lecture Notes in Computer Science
Field
DocType
Volume
Memetic algorithm,Online algorithm,Scheduling (computing),Computer science,Robustness (computer science),Artificial intelligence,Operator (computer programming),Mathematical optimization,Crossover,Domain knowledge,Algorithm,Local search (optimization),Machine learning
Conference
8994
ISSN
Citations 
PageRank 
0302-9743
1
0.35
References 
Authors
18
3
Name
Order
Citations
PageRank
Mustafa Misir151.76
Stephanus Daniel Handoko2959.18
Hoong Chuin Lau373991.69