copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas
This work offers a software tool for researchers in optimization and machine learning to explore copula-based EDAs, but it is incremental as it packages existing methods rather than introducing new algorithms.
The authors introduced an R package called copulaedas that implements and facilitates the study of estimation of distribution algorithms (EDAs) based on copulas, providing tools for algorithm integration, benchmarking, and empirical comparison on optimization problems.
The use of copula-based models in EDAs (estimation of distribution algorithms) is currently an active area of research. In this context, the copulaedas package for R provides a platform where EDAs based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of well-known optimization problems, and utility functions to study the performance of the algorithms. Newly developed EDAs can be easily integrated into the package by extending an S4 class with generic functions for their main components. This paper presents copulaedas by providing an overview of EDAs based on copulas, a description of the implementation of the package, and an illustration of its use through examples. The examples include running the EDAs defined in the package, implementing new algorithms, and performing an empirical study to compare the behavior of different algorithms on benchmark functions and a real-world problem.