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Non-parametric model of the space of continuous black-box optimization problems

Published: 15 July 2017 Publication History

Abstract

Exploratory Landscape Analysis are data driven methods used for automated algorithm selection in continuous black-box optimization. Most of these methods follow strong assumptions that limit their characterization power, or loose information by compressing the data into a few scalar features. A more flexible approach is to avoid explicit measuring and comparing of specific structures. In this paper we present a proof-of-concept for a more general method, which produces non-parametric models of the space of problems. Using non-metric multidimensional scaling, we generate synthetic features for each problem, which could replace or complement the existing ones. We demonstrate approaches to produce algorithm recommendations and visual representations of the space. To validate the model, we compare our results with those obtained through existing methods, which show that our models have competitive performance.

References

[1]
N. Hansen, A. Auger, S. Finck, and R. Ros. 2014. Real-Parameter Black-Box Optimization Benchmarking BBOB-2010: Experimental Setup. Technical Report RR-7215. INRIA.
[2]
S. Kullback and RA. Leibler. 1951. On Information and Sufficiency. Ann. Math. Stat. 22, 1 (1951), 79--86.
[3]
M. Lunacek and D. Whitley. 2006. The dispersion metric and the CMA evolution strategy. In GECCO '06. ACM, New York, NY, USA, 477--484.
[4]
J. Marin. 2012. How landscape ruggedness influences the performance of real-coded algorithms: a comparative study. Soft Comput. 16, 4 (2012), 683--698.
[5]
O. Mersmann, B. Bischl, H. Trautmann, M. Preuß, C. Weihs, and G. Rudolph. 2011. Exploratory landscape analysis. In GECCO '11. ACM, New York, NY, USA, 829--836.
[6]
R. Morgan and M. Gallagher. 2017. Analysing and characterising optimization problems using length scale. Soft Comput. 21, 7 (2017), 1735--1752.
[7]
M.A. Mu noz, M. Kirley, and S.K. Halgamuge. 2015. Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. 19, 1 (2015), 74--87.
[8]
D.I. Seo and B.R. Moon. 2007. An Information-Theoretic Analysis on the Interactions of Variables in Combinatorial Optimization Problems. Evol. Comput. 15, 2 (2007), 169--198.
[9]
Z. Szabó. 2014. Information Theoretical Estimators Toolbox. J. Mach. Learn. Res. 15 (2014), 283--287.
[10]
L. van der Maaten. 2014. Accelerating t-SNE using Tree-Based Algorithms. J. Mach. Learn. Res. 15 (2014), 3221--3245.

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 15 July 2017

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Author Tags

  1. black-box optimization
  2. continuous optimization
  3. exploratory landscape analysis

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