Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
Pascal Kerschke

TL;DR
This paper introduces 'flacco', an R-package that consolidates various Exploratory Landscape Analysis features for continuous and constrained optimization problems, facilitating landscape understanding and aiding algorithm selection.
Contribution
The paper presents a comprehensive R-package that simplifies access to multiple ELA features and visualization tools, streamlining landscape analysis for optimization problems.
Findings
Provides a unified platform for ELA features in R
Includes visualization techniques for landscape understanding
Offers a user-friendly GUI for non-R users
Abstract
Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) - a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself - is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been - if at all - spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Environmental Impact and Sustainability · Probabilistic and Robust Engineering Design
