7 citations · 10 across the 12 of their papers we have counts for
11 papers · 1 filter
The Modular CMA-ES: A Framework for Modern Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continu…
Integer Natural Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
While contemporary Evolution Strategies handle integer optimization problems effectively, their adaptation mechanism is grounded in -based Gaussian models, which are not na…
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
Jacob de Nobel, Diederick Vermetten, Hao Wang +3
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception. Many lines of reasoning have been given for this strong dependen…
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
Diederick Vermetten, Jeroen Rook, Oliver L. Preuß +5
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anyti…
Sampling in CMA-ES: Low Numbers of Low Discrepancy Points
Jacob de Nobel, Diederick Vermetten, Thomas H. W. Bäck +1
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the most successful examples of a derandomized evolution strategy. However, it still relies on randomly sampl…
Avoiding Redundant Restarts in Multimodal Global Optimization
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Naïve restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant func…
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
André Thomaser, Jacob de Nobel, Diederick Vermetten +3
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Furong Ye, Frank Neumann, Jacob de Nobel +2
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algor…
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
Jacob de Nobel, Furong Ye, Diederick Vermetten +3
We present IOHexperimenter, the experimentation module of the IOHprofiler project, which aims at providing an easy-to-use and highly customizable toolbox for benchmarking iterative…
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
Jacob de Nobel, Hao Wang, Thomas Bäck
In this study, we analyze behaviours of the well-known CMA-ES by extracting the time-series features on its dynamic strategy parameters. An extensive experiment was conducted on tw…
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
Jacob de Nobel, Diederick Vermetten, Hao Wang +2
Introducing new algorithmic ideas is a key part of the continuous improvement of existing optimization algorithms. However, when introducing a new component into an existing algori…