ABSTRACT
Finding the optimal solution for a given problem has always been an intriguing goal and a key for reaching this goal is sound knowledge of the problem at hand. In case of single-objective, continuous, global optimization problems, such knowledge can be gained by Exploratory Landscape Analysis (ELA), which computes features that quantify the problem's landscape prior to optimization. Due to the various backgrounds of researches that developed such features, there nowadays exist numerous implementations of feature sets across multiple programming languages, which is a blessing and burden at the same time.
The recently developed R-package flacco takes multiple of these feature sets (from the different packages and languages) and combines them within a single R-package. While this is very beneficial for R-users, users of other programming languages are left out. Within this paper, we introduce flaccogui, a graphical user interface that does not only make flacco more user-friendly, but due to a platform-independent web-application also allows researchers that are not familiar with R to perform ELA and benefit of the advantages of flacco.
- Tinus Abell, Yuri Malitsky, and Kevin Tierney. 2013. Features for Exploiting Black-Box Optimization Problem Structure. In Proceedings of 7th International Conference on Learning and Intelligent Optimization (LION), Giuseppe Nicosia and Panos Pardalos (Eds.). Springer, 30 -- 36. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Brian K Beachkofski and Ramana V Grandhi. 2002. Improved Distributed Hypercube Sampling. In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference.Google Scholar
- Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Thomas Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger Hendrik Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, and Joaquin Vanschoren. 2016. ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence 237 (2016), 41 -- 58. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Bernd Bischl, Olaf Mersmann, Heike Trautmann, and Mike Preuss. 2012. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO '12). ACM, New York, NY, USA, 313--320. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Jakob Bossek. 2016. smoof: Single and Multi-Objective Optimization Test Functions. https://github.com/jakobbossek/smoof R-package version 1.4.Google Scholar
- Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie, and Jonathan McPherson. 2016. shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny R package version 0.14.1.Google Scholar
- Fabio Daolio, Sébastien Verel, Gabriela Ochoa, and Marco Tomassini. 2012. Local optima networks and the performance of iterated local search. In Proceedings of the 14th annual conference on Genetic and evolutionary computation. ACM, 369--376. Google ScholarDigital Library
- Deon Garrett and Dipankar Dasgupta. 2007. Multiobjective Landscape Analysis and the Generalized Assignment Problem. In Proceedings of 2nd International Conference on Learning and Intelligent Optimization (LION), Vittorio Maniezzo, Roberto Battiti, and Jean-Paul Watson (Eds.). Lecture Notes in Computer Science, Vol. 5313. Springer, 110 -- 124. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Nikolaus Hansen, Anne Auger, Steffen Finck, and Raymond Ros. 2010. Real-Parameter Black-Box Optimization Benchmarking 2010: Experimental Setup. Technical Report RR-7215. INRIA. http://hal.inria.fr/docs/00/46/24/81/PDF/RR-7215.pdfGoogle Scholar
- Frank Hutter, Lin Xu, Holger H. Hoos, and Kevin Leyton-Brown. 2014. Algorithm runtime prediction: Methods & evaluation. Journal of Artificial Intelligence 206, 0 (2014), 79--111. Google ScholarDigital Library
- Terry Jones. 1995. Evolutionary algorithms, fitness landscapes and search. Ph.D. Dissertation. Citeseer.Google Scholar
- Pascal Kerschke. 2017. flacco: Feature-Based Landscape Analysis of Continuous and Constraint Optimization Problems. https://github.com/kerschke/flacco R-package version 1.5.Google Scholar
- Pascal Kerschke, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian-Qiao Sun, Christian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. 2014. Cell Mapping Techniques for Exploratory Landscape Analysis. In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Springer, 115--131. DOI:http://dx.doi.org/Google Scholar
- Pascal Kerschke, Mike Preuss, Simon Wessing, and Heike Trautmann. 2016. Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. In Proceedings of the 18th Annual Conference on Genetic and Evolutionary Computation. ACM. Google ScholarDigital Library
- Pascal Kerschke and Heike Trautmann. 2016. The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE.Google ScholarCross Ref
- Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André Deutz, Heike Trautmann, and Michael T. M. Emmerich. 2016. Towards Analyzing Multimodality of Multiobjective Landscapes. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV) (Lecture Notes in Computer Science), Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, and Ben Paechter (Eds.). Springer, 962--972.Google Scholar
- Lars Kotthoff, Pascal Kerschke, Holger Hendrik Hoos, and Heike Trautmann. 2015. Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection. In Proceedings of 9th International Conference on Learning and Intelligent Optimization (LION) (Lecture Notes in Computer Science), Clarisse Dhaenens, Laetitia Jourdan, and Marie-Eléonore Marmion (Eds.), Vol. 8994. Springer, 202 -- 217. DOI:http://dx.doi.org/Google ScholarCross Ref
- Arnaud Liefooghe, Sébastien Verel, Fabio Daolio, Hernán Aguirre, and Kiyoshi Tanaka. 2015. A Feature-Based Performance Analysis in Evolutionary Multi-objective Optimization. In Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization (EMO), António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos A. Coello Coello (Eds.). Lecture Notes in Computer Science, Vol. 9019. Springer, 95 -- 109. DOI:http://dx.doi.org/Google Scholar
- Monte Lunacek and Darrell Whitley. 2006. The Dispersion Metric and the CMA Evolution Strategy. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. ACM, 477--484. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Katherine M Malan and Andries P Engelbrecht. 2009. Quantifying ruggedness of continuous landscapes using entropy. In Evolutionary Computation, 2009. CEC'09. IEEE Congress on. IEEE, 1440 -- 1447. DOI:http://dx.doi.org/ Google ScholarDigital Library
- MATLAB. 2013. Version 8.2.0 (R2013b). The Math Works Inc., Natick, MA, USA.Google Scholar
- Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. 2011. Exploratory Landscape Analysis. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO '11). ACM, New York, NY, USA, 829--836. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek, and Frank Neumann. 2013. A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence 69, 2 (2013), 151--182. Google ScholarDigital Library
- Olaf Mersmann, Mike Preuss, and Heike Trautmann. 2010. Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (Lecture Notes in Computer Science 6238), R. Schaefer and others (Eds.). Springer, 71--80. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Rachael Morgan and Marcus Gallagher. 2015. Analysing and Characterising Optimization Problems Using Length Scale. Soft Computing (2015), 1 -- 18. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Christian L. Müller and Ivo F. Sbalzarini. 2011. Global Characterization of the CEC 2005 Fitness Landscapes Using Fitness-Distance Analysis. In Applications of Evolutionary Computation. Springer, 294 -- 303. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Mario Andrés Muñoz, Michael Kirley, and Saman K Halgamuge. 2015. Exploratory Landscape Analysis of Continuous Space Optimization Problems using Information Content. Evolutionary Computation, IEEE Transactions on 19, 1 (2015), 74--87. DOI:http://dx.doi.org/Google Scholar
- Mario Andrés Muñoz, Yuan Sun, Michael Kirley, and Saman K. Halgamuge. 2015. Algorithm Selection for Black-Box Continuous Optimization Problems: A Survey on Methods and Challenges. Information Sciences 317 (2015), 224 -- 245. DOI:http://dx.doi.org/ Google ScholarDigital Library
- Eugene Nudelman, Kevin Leyton-Brown, Holger H. Hoos, Alex Devkar, and Yoav Shoham. 2004. Understanding Random SAT: Beyond the Clauses-to-Variables Ratio. Springer, 438--452. Google ScholarDigital Library
- Josef Pihera and Nysret Musliu. 2014. Application of Machine Learning to Algorithm Selection for TSP. In Proceedings of the IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE Computer Society, 47--54. Google ScholarDigital Library
- R Core Team. 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/Google Scholar
- John R Rice. 1976. The Algorithm Selection Problem. Advances in Computers 15 (1976), 65--118.Google ScholarCross Ref
- Guido VanRossum and The Python Development Team. 2015. The Python Language Reference - Release 3.5.0. Python Software Foundation.Google Scholar
Index Terms
- flaccogui: exploratory landscape analysis for everyone
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