Search Results for author: Anna V. Kononova

Found 21 papers, 4 papers with code

MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing

1 code implementation5 Sep 2023 Lucas Correia, Jan-Christoph Goos, Philipp Klein, Thomas Bäck, Anna V. Kononova

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.

Anomaly Detection Time Series

Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite

no code implementations24 May 2023 Diederick Vermetten, Manuel López-Ibáñez, Olaf Mersmann, Richard Allmendinger, Anna V. Kononova

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

no code implementations24 May 2023 Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein

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

no code implementations20 May 2023 Mădălina-Andreea Mitran, Anna V. Kononova, Fabio Caraffini, Daniela Zaharie

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

no code implementations25 Apr 2023 André Thomaser, Jacob de Nobel, Diederick Vermetten, Furong Ye, Thomas Bäck, Anna V. Kononova

In this work, we use the notion of the resolution of continuous variables to discretize problems from the continuous domain.

Modular Differential Evolution

no code implementations19 Apr 2023 Diederick Vermetten, Fabio Caraffini, Anna V. Kononova, Thomas Bäck

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

1 code implementation4 Apr 2023 Bas van Stein, Diederick Vermetten, Fabio Caraffini, Anna V. Kononova

Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms.

Explainable Artificial Intelligence (XAI)

Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

no code implementations13 Dec 2022 Qi Huang, Roy de Winter, Bas van Stein, Thomas Bäck, Anna V. Kononova

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

no code implementations29 Nov 2022 Fu Xing Long, Diederick Vermetten, Bas van Stein, Anna V. Kononova

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

no code implementations14 Nov 2022 Jacob de Nobel, Anna V. Kononova, Jeroen Briaire, Johan Frijns, Thomas Bäck

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

1 code implementation7 Mar 2022 Anna V. Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A. Mitran, Daniela Zaharie

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

1 code implementation17 Jan 2022 Koen Ponse, Anna V. Kononova, Maria Loleyt, Bas van Stein

We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.

BIG-bench Machine Learning Symmetry Detection

Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution

no code implementations14 May 2021 Rick Boks, Anna V. Kononova, Hao Wang

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.


Emergence of Structural Bias in Differential Evolution

no code implementations10 May 2021 Bas van Stein, Fabio Caraffini, Anna V. Kononova

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved.

Is there Anisotropy in Structural Bias?

no code implementations10 May 2021 Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck

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.

Differential evolution outside the box

no code implementations22 Apr 2020 Anna V. Kononova, Fabio Caraffini, Thomas Bäck

A wide range of popular Differential Evolution configurations is considered in this study.

Infeasibility and structural bias in Differential Evolution

no code implementations18 Jan 2019 Fabio Caraffini, Anna V. Kononova, David Corne

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

no code implementations22 Aug 2014 Anna V. Kononova, David W. Corne, Philippe De Wilde, Vsevolod Shneer, Fabio Caraffini

Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty.


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