Generative Adversarial Construction of Parallel Portfolios

Since automatic algorithm configuration methods have been very effective, recently there is increasing research interest in utilizing them for automatic solver construction, resulting in several notable approaches. For these approaches, a basic assumption is that the given training set could suffici...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 52; no. 2; pp. 784 - 795
Main Authors: Liu, Shengcai, Tang, Ke, Yao, Xin
Format: Journal Article
Language:English
Published: United States IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automatic portfolio construction (APC)
Boolean algebra
generative adversarial approach
Generative adversarial networks
parallel algorithm portfolio
Parallel algorithms
parameter tuning
Portfolios
Solvers
Training
Training data
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Since automatic algorithm configuration methods have been very effective, recently there is increasing research interest in utilizing them for automatic solver construction, resulting in several notable approaches. For these approaches, a basic assumption is that the given training set could sufficiently represent the target use cases such that the constructed solvers can generalize well. However, such an assumption does not always hold in practice since in some cases, we might only have scarce and biased training data. This article studies effective construction approaches for the parallel algorithm portfolios that are less affected in these cases. Unlike previous approaches, the proposed approach simultaneously considers instance generation and portfolio construction in an adversarial process, in which the aim of the former is to generate instances that are challenging for the current portfolio, while the aim of the latter is to find a new component solver for the portfolio to better solve the newly generated instances. Applied to two widely studied problem domains, that is, the Boolean satisfiability problems (SAT) and the traveling salesman problems (TSPs), the proposed approach identified parallel portfolios with much better generalization than the ones generated by the existing approaches when the training data were scarce and biased. Moreover, it was further demonstrated that the generated portfolios could even rival the state-of-the-art manually designed parallel solvers.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.2984546