GECCO 2017 Data on Algorithm Selection for the quadratic assignment problem QAP

GECCO 2017 Data

Beham, A., Affenzeller, M., & Wagner, S. (2017). Instance-based algorithm selection on quadratic assignment problem landscapes. In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1471-1478). (GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion). ACM Sigevo. https://doi.org/10.1145/3067695.3082513

@inproceedings{10.1145/3067695.3082513,
author = {Beham, Andreas and Affenzeller, Michael and Wagner, Stefan},
title = {Instance-Based Algorithm Selection on Quadratic Assignment Problem Landscapes},
year = {2017},
isbn = {9781450349390},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3067695.3082513},
doi = {10.1145/3067695.3082513},
abstract = {Among the many applications of fitness landscape analysis a prominent example is algorithm selection. The no-free-lunch (NFL) theorem has put a limit on the general applicability of heuristic search methods. Improved methods can only be found by specialization to certain problem characteristics which limits their application to other problems. This creates a very interesting and dynamic field for algorithm development. However, this also leads to the definition of a large range of different algorithms that are hard to compare exhaustively. An additional challenge is posed by the fact that algorithms have parameters and thus to each algorithm there may be a large number of instances. In this work the application of algorithm selection to problem instances of the quadratic assignment problem (QAP) is discussed.},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages = {1471–1478},
numpages = {8},
keywords = {algorithm selection, quadratic assignment problem, fitness landscapes, feature extraction},
location = {Berlin, Germany},
series = {GECCO '17}
}

x_flaperfdata_proj_basic.csv

x_qap_ert.csv