Single and Multi-Objective Optimization Test Functions
TL;DR: The smoof package implements a large set of test functions and test function generators for both the single and multi-objective case in continuous optimization and provides functions to easily create own test functions.
read more
Abstract: Benchmarking algorithms for optimization problems usually is carried out by running the algorithms under consideration on a diverse set of benchmark or test functions. A vast variety of test functions was proposed by researchers and is being used for investigations in the literature. The smoof package implements a large set of test functions and test function generators for both the singleand multi-objective case in continuous optimization and provides functions to easily create own test functions. Moreover, the package offers some additional helper methods, which can be used in the context of optimization.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Automated Algorithm Selection: Survey and Perspectives
TL;DR: This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection.
463
Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
Pascal Kerschke,Heike Trautmann +1 more
TL;DR: In this paper, an algorithm selection model for continuous black-box optimization problems is presented, based on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications.
189
Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
TL;DR: The benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment was demonstrated and it was concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps.
137
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
Pascal Kerschke,Heike Trautmann +1 more
TL;DR: Flacco is introduced, an R-package for feature-based landscape analysis of continuous and constrained optimization problems that 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.
136
Single and Multi-Objective Optimization Test Functions
TL;DR: The smoof package implements a large set of test functions and test function generators for both the single and multi-objective case in continuous optimization and provides functions to easily create own test functions.
References
•Book
ggplot2: Elegant Graphics for Data Analysis
Hadley Wickham
- 13 Aug 2009
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
•Book
Evolutionary algorithms for solving multi-objective problems
Gary B. Lamont,David A. Van Veldhuizen +1 more
- 30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Comparison of multiobjective evolutionary algorithms: empirical results
Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele +2 more
- 01 Jan 1999
TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
3.9K
A review of multiobjective test problems and a scalable test problem toolkit
TL;DR: This paper systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems, and presents a flexible toolkit for constructing well-designed test problems.