Automatic algorithm selection for the quadratic assignment problem using fitness landscape analysis

Authors:
Erik Pitzer;Andreas Beham;Michael Affenzeller
Affiliations:
School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria;School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria;School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria
Venue:
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2013

Citing 9
Cited 1

What makes an optimization problem hard?

Complexity
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
QAPLIB – A Quadratic Assignment ProblemLibrary

Journal of Global Optimization
Information Characteristics and the Structure of Landscapes

Evolutionary Computation
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
Multistart tabu search and diversification strategies for the quadratic assignment problem

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Paper: Robust taboo search for the quadratic assignment problem

Parallel Computing
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem

IEEE Transactions on Evolutionary Computation
Generic hardness estimation using fitness and parameter landscapes applied to robust taboo search and the quadratic assignment problem

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Recent advances in problem understanding: changes in the landscape a year on

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.