irace - Iterated Racing for Automatic Algorithm Configuration

Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem. M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle, and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.

Last updated 4 days ago

algorithm-configurationhyperparameter-tuningiraceoptimization-algorithms

10.22 score 62 stars 1 dependents 93 scripts 2.0k downloads

moocore - Core Mathematical Functions for Multi-Objective Optimization

Fast implementation of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

Last updated 18 days ago

6.03 score 9 stars 4 dependents 7 scripts 648 downloads

iraceplot - Plots for Visualizing the Data Produced by the 'irace' Package

Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.

Last updated 24 days ago

iraceparameter-tuning

5.74 score 5 stars 7 scripts 383 downloads

eaf - Plots of the Empirical Attainment Function

Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.

Last updated 5 months ago

eafeaf-differencesepsilonhypervolumeinverted-generational-distancemultiobjective-optimizationsummary-attainment-surfacesvisualizationgsl

5.22 score 17 stars 1 dependents 32 scripts 1.0k downloads