The R-Package FLACCO for exploratory landscape analysis with applications to multi-objective optimization problems

Exploratory Landscape Analysis (ELA) aims at understanding characteristics of single-objective continuous (black-box) optimization problems in an automated way. Moreover, the approach provides the basis for constructing algorithm selection models for unseen problem instances. Recently, it has gained increasing attention and numerical features have been designed by various research groups. This paper introduces the R-Package FLACCO which makes all relevant features available in a unified framework together with efficient helper functions. Moreover, a case study which gives perspectives to ELA for multi-objective optimization problems is presented.

[1]  Dipankar Dasgupta,et al.  Multiobjective Landscape Analysis and the Generalized Assignment Problem , 2008, LION.

[2]  Marcus Gallagher,et al.  Multi-layer Perceptron Error Surfaces: Visualization, Structure and Modelling , 2000 .

[3]  Jakob Bossek,et al.  smoof: Single- and Multi-Objective Optimization Test Functions , 2017, R J..

[4]  Christian L. Müller,et al.  Global Characterization of the CEC 2005 Fitness Landscapes Using Fitness-Distance Analysis , 2011, EvoApplications.

[5]  Heike Trautmann,et al.  Detecting Funnel Structures by Means of Exploratory Landscape Analysis , 2015, GECCO.

[6]  Pascal Kerschke,et al.  Feature-Based Landscape Analysis of Continuous and ConstraintOptimization Problems , 2016 .

[7]  Heike Trautmann,et al.  Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis , 2010, PPSN.

[8]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Sébastien Vérel,et al.  A Feature-Based Performance Analysis in Evolutionary Multiobjective Optimization , 2015, EMO.

[11]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[12]  Arnaud Liefooghe,et al.  New Results - On the Structure of Multiobjective Combinatorial Search Space: Multiobjective NK-Landscapes with Correlated Objectives , 2011 .

[13]  Sébastien Vérel,et al.  Set-based multiobjective fitness landscapes: a preliminary study , 2011, GECCO '11.

[14]  L. Darrell Whitley,et al.  The dispersion metric and the CMA evolution strategy , 2006, GECCO.

[15]  Léopold Simar,et al.  Canonical Correlation Analysis , 2015 .

[16]  Saman K. Halgamuge,et al.  Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content , 2015, IEEE Transactions on Evolutionary Computation.

[17]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[18]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[19]  Yuri Malitsky,et al.  Features for Exploiting Black-Box Optimization Problem Structure , 2013, LION.

[20]  Bernd Bischl,et al.  Exploratory landscape analysis , 2011, GECCO '11.

[21]  Bernd Bischl,et al.  Cell Mapping Techniques for Exploratory Landscape Analysis , 2014 .

[22]  W. Härdle,et al.  Applied Multivariate Statistical Analysis , 2003 .

[23]  Marcus Gallagher,et al.  Analysing and characterising optimization problems using length scale , 2017, Soft Comput..

[24]  Mario A. Muñoz,et al.  Landscape characterization of numerical optimization problems using biased scattered data , 2012, 2012 IEEE Congress on Evolutionary Computation.

[25]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[26]  John R. Rice,et al.  The Algorithm Selection Problem , 1976, Adv. Comput..

[27]  Michael T. Wolfinger,et al.  Barrier Trees of Degenerate Landscapes , 2002 .

[28]  Bernd Bischl,et al.  Algorithm selection based on exploratory landscape analysis and cost-sensitive learning , 2012, GECCO '12.

[29]  Mario A. Muñoz,et al.  Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges , 2015, Inf. Sci..

[30]  Heike Trautmann,et al.  Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models , 2016, GECCO.