Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers

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Contributors

Abstract

Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.

Details

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference (GECCO) Companion
Publication statusPublished - 6 Jul 2018
Peer-reviewedYes

External IDs

Scopus 85051526664