7 citations · 10 across the 12 of their papers we have counts for
16 papers
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…
Design of efficient high-order immersed metagratings using an evolutionary algorithm
Dhwanil Patel, Jacob de Nobel, Anna V. Kononova +2
Immersed reflection gratings improve spectral resolving power by enabling diffraction within a high refractive index medium. This principle has been widely adopted to make grating…
From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Jacob de Nobel, Jeroen J. Briaire, Thomas H. W. Baeck +2
We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. Th…
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…
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
Annie Wong, Jacob de Nobel, Thomas Bäck +2
Although deep reinforcement learning methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex, and training time…
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…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
Jacob de Nobel, Anna V. Kononova, Jeroen Briaire +2
Performing simulations with a realistic biophysical auditory nerve fiber model can be very time consuming, due to the complex nature of the calculations involved. Here, a surrogate…
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…