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The Generation of Experimental Data for Computational Testing in Optimization

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Experimental Methods for the Analysis of Optimization Algorithms

Abstract

This chapter discusses approaches to generating synthetic data for use in scientific experiments. In many diverse scientific fields, the lack of availability, high cost or inconvenience of the collection of real-world data motivates the generation of synthetic data. In many experiments, the method chosen to generate synthetic data can significantly affect the results of an experiment. Unfortunately, the scientific literature does not contain general protocols for how synthetic data should be generated. The purpose of this chapter is to rectify that deficiency. The protocol we propose is based on several generation principles. These principles motivate and organize the data generation process. The principles are operationalized by generation properties. Then, together with information about the features of the application and of the experiment, the properties are used to construct a data generation scheme. Finally, we suggest procedures for validating the synthetic data generated. The usefulness of our protocol is illustrated by a discussion of numerous applications of data generation from the optimization literature. This discussion identifies examples of both good and bad data generation practice as it relates to our protocol.

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Acknowledgements

This research is supported by the Summer Fellowship Program of the Fisher College of Business, The Ohio State University, to the first author. Helpful comments were provided by Tito Homem-de-Mello and two anonymous reviewers.

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Authors and Affiliations

  1. Department of Management Sciences, The Ohio State University, Columbus, Ohio, USA

    Nicholas G. Hall

  2. Department of Integrated Systems Engineering, The Ohio State University, Columbus, Ohio, USA

    Marc E. Posner

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  1. Nicholas G. Hall
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  2. Marc E. Posner
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Correspondence to Nicholas G. Hall or Marc E. Posner .

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Editors and Affiliations

  1. Institute of Computer Science, Faculty of Computer Science, Cologne University of Applied Sciences, Steinmüllerallee 1, Gummersbach, 51643, Germany

    Thomas Bartz-Beielstein

  2. , Department of Mathematics, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark

    Marco Chiarandini

  3. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, 3030-290, Portugal

    Luís Paquete

  4. Algorithm Engineering, TU Dortmund, Otto-Hahn-Str. 14, Dortmund, 44227, Germany

    Mike Preuss

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Hall, N.G., Posner, M.E. (2010). The Generation of Experimental Data for Computational Testing in Optimization. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds) Experimental Methods for the Analysis of Optimization Algorithms. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02538-9_4

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