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

08/17/2017
by   Pascal Kerschke, et al.
0

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 introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related "Algorithm Selection Problem (ASP)", it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform - even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package's build-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI), which facilitates the usage of the package - especially for people who are not familiar with R - making it a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.

READ FULL TEXT

page 2

page 10

page 14

page 22

page 23

page 25

research
11/24/2017

Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning

In this paper, we build upon previous work on designing informative and ...
research
04/12/2022

A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

Exploratory Landscape Analysis is a powerful technique for numerically c...
research
07/30/2022

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Hyperparameter optimization (HPO) is a key component of machine learning...
research
12/22/2019

Black Box Algorithm Selection by Convolutional Neural Network

Although a large number of optimization algorithms have been proposed fo...
research
05/24/2023

Challenges of ELA-guided Function Evolution using Genetic Programming

Within the optimization community, the question of how to generate new o...
research
04/27/2021

A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems

When designing a benchmark problem set, it is important to create a set ...

Please sign up or login with your details

Forgot password? Click here to reset