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Found 34 papers, 8 papers with code
A Dataset for Evaluating Online Anomaly Detection Approaches for Discrete Multivariate Time Series
To cater for both unsupervised and semi-supervised anomaly detection settings, as well as time series generation and forecasting, we make different versions of the dataset available, where training and test subsets are offered in contaminated and clean versions, depending on the task.
AMaze: An intuitive benchmark generator for fast prototyping of generalizable agents
Agents were trained under three different regimes (one-shot, scaffolding, interactive), and the results showed that the latter two cases outperform direct training in terms of generalization capabilities.
Selection of Filters for Photonic Crystal Spectrometer Using Domain-Aware Evolutionary Algorithms
This work addresses the critical challenge of optimal filter selection for a novel trace gas measurement device.
Sampling in CMA-ES: Low Numbers of Low Discrepancy Points
For lower dimensionalities (below 10), we find that using as little as 32 unique low discrepancy points performs similar or better than uniform sampling.
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties.
Interactive embodied evolution for socially adept Artificial General Creatures
We introduce here the concept of Artificial General Creatures (AGC) which encompasses "robotic or virtual agents with a wide enough range of capabilities to ensure their continued survival".
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data.
Avoiding Redundant Restarts in Multimodal Global Optimization
Na\"ive restarts of global optimization solvers when operating on multimodal search landscapes may resemble the Coupon's Collector Problem, with a potential to waste significant function evaluations budget on revisiting the same basins of attractions.
A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes.
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points of discussion within the wider community.
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems.
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
We benchmark both deep policy networks and networks consisting of a single linear layer from observations to actions for three gradient-based methods, such as Proximal Policy Optimization.
Explainable Benchmarking for Iterative Optimization Heuristics
Introducing the IOH-Xplainer software framework, for analyzing and understanding the performance of various optimization algorithms and the impact of their different components and hyper-parameters.
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing
A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity.
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES.
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite
This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated.
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.
Modular Differential Evolution
Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants.
Deep-BIAS: Detecting Structural Bias using Explainable AI
Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems.
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency.
The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond
Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity.
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.
Deep Multiagent Reinforcement Learning: Challenges and Directions
This paper surveys the field of deep multiagent reinforcement learning.
Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge
Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge.
Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly.
Is there Anisotropy in Structural Bias?
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Emergence of Structural Bias in Differential Evolution
Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.
Differential evolution outside the box
A wide range of popular Differential Evolution configurations is considered in this study.
Infeasibility and structural bias in Differential Evolution
This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values.
Structural bias in population-based algorithms
Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.
