A case study of algorithm selection for the traveling thief problem

Wagner, Markus; Lindauer, Marius; Mısır, Mustafa; Nallaperuma, Samadhi; Hutter, Frank

doi:10.1007/s10732-017-9328-y

Cited by 63 publications

(31 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Effectively, this will let us test which tuned corpus-configuration performs well on others. A similar approach was used by Wagner et al to investigate the importance of instance features in the context of perinstance configuration of solvers for the minimum vertex cover problem [25], for the traveling salesperson problem [26], and for the traveling thief problem [27].…”

Section: Per-corpus Configurationmentioning

confidence: 99%

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Wagner¹

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

Self Cite

View full text Add to dashboard Cite

Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that aims to explain the structure of a corpus by grouping texts. LDA requires multiple parameters to work well, and there are only rough and sometimes conflicting guidelines available on how these parameters should be set. In this paper, we contribute (i) a broad study of parameters to arrive at good local optima for GitHub and Stack Overflow text corpora, (ii) an a-posteriori characterisation of text corpora related to eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration. We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different characteristics and require different configurations to achieve good model fit, and (3) we can predict good configurations for unseen corpora reliably. These findings support researchers and practitioners in efficiently determining suitable configurations for topic modelling when analysing textual data contained in software repositories.

show abstract

Section: Per-corpus Configurationmentioning

confidence: 99%

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Wagner¹

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

Self Cite

View full text Add to dashboard Cite

show abstract

“…To compare the outcomes of the different approaches based on the final populations Π of tours, we calculate the hypervolumes for the surface of resulting non-dominated solutions. We also store the corresponding total reward in order to compare with the results from the state-of-the-art single-objective approach: MA2B [9] (see comparison in [28]).…”

Section: Results and Analysismentioning

confidence: 99%

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

Polyakovskiy

Wagner

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

Self Cite

View full text Add to dashboard Cite

This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical N P-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying Packing-While-Travelling problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.

show abstract

“…Hutter et al (2014b) introduced 95 features for MIP, including problem type and size, variable-constraint graph, linear constraint matrix, objective function and LPbased features. For the travelling thief problem (TTP), which can be seen as a combination of the TSP and knapsack problem (KP), algorithm selection has been studied by Wagner et al (2017). They used 48 TSP and four KP features, plus three parameters of the TTP that connect the TSP and KP parts of the problem.…”

Section: Performance Measuresmentioning

confidence: 99%

Automated Algorithm Selection: Survey and Perspectives

Kerschke

Hoos

Neumann

et al. 2019

Evolutionary Computation

318

184

View full text Add to dashboard Cite

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Perinstance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems.This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective perinstance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

show abstract

A case study of algorithm selection for the traveling thief problem

Cited by 63 publications

References 53 publications

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Predicting Good Configurations for GitHub and Stack Overflow Topic Models

Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

Automated Algorithm Selection: Survey and Perspectives

Contact Info

Product

Resources

About