TY - GEN
T1 - Automatic algorithm selection for the quadratic assignment problem using fitness landscape analysis
AU - Pitzer, Erik
AU - Beham, Andreas
AU - Affenzeller, Michael
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the application more and more, new problems are investigated, where little or no experience has been collected. In an attempt to provide a more general criterion for algorithm and parameter selection other than "it works better than something else we tried", sophisticated problem analysis and classification schemes are employed. In this work, we combine several of these analysis methods and evaluate the suitability of fitness landscape analysis for the task of algorithm selection.
AB - In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the application more and more, new problems are investigated, where little or no experience has been collected. In an attempt to provide a more general criterion for algorithm and parameter selection other than "it works better than something else we tried", sophisticated problem analysis and classification schemes are employed. In this work, we combine several of these analysis methods and evaluate the suitability of fitness landscape analysis for the task of algorithm selection.
KW - Fitness Landscape Analysis
KW - Problem Understanding
KW - Quadratic Assignment Problem
KW - Robust Taboo Search
KW - Variable Neighborhood Search
UR - http://www.scopus.com/inward/record.url?scp=84875092241&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37198-1_10
DO - 10.1007/978-3-642-37198-1_10
M3 - Conference contribution
SN - 9783642371974
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 120
BT - Evolutionary Computation in Combinatorial Optimization - 13th European Conference, EvoCOP 2013, Proceedings
PB - Springer
T2 - 13th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2013
Y2 - 3 April 2013 through 5 April 2013
ER -