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Simulation-based Test Functions for Optimization Algorithms

  • When designing or developing optimization algorithms, test functions are crucial to evaluate performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world applications. Previously, test functions with real-world relevance were generated by training a machine learning model based on real-world data. The model estimation is used as a test function. We propose a more principled approach using simulation instead of estimation. Thus, relevant and varied test functions are created which represent the behavior of real-world fitness landscapes. Importantly, estimation can lead to excessively smooth test functions while simulation may avoid this pitfall. Moreover, the simulation can be conditioned by the data, so that the simulation reproduces the training data but features diverse behavior in unobserved regions of the search space. The proposed test function generator is illustrated with an intuitive, one-dimensional example. To demonstrate the utility of this approach it is applied to a protein sequence optimization problem. This application demonstrates the advantages as well as practical limits of simulation-based test functions.

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Metadaten
Author:Martin Zaefferer, Andreas Fischbach, Boris Naujoks, Thomas Bartz-BeielsteinGND
URN:urn:nbn:de:hbz:832-cos4-4777
Series (Serial Number):CIplus (3/2017)
Document Type:Working Paper
Language:English
Year of Completion:2017
Release Date:2017/05/10
Tag:Modeling; Optimization; Simulation; Test function generator
Pagenumber:12
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:I. Computing Methodologies / I.6 SIMULATION AND MODELING (G.3)
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung