Search Results for author:
Found 79 papers, 21 papers with code
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluation of the algorithms' performance by means of the distribution on the fixed-target running time and the fixed-budget function values.
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization.
Automatic Configuration of Deep Neural Networks with EGO
In this paper an Efficient Global Optimization (EGO) algorithm is adapted to automatically optimize and configure convolutional neural network architectures.
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.
Discovering outstanding subgroup lists for numeric targets using MDL
We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists.
Robust subgroup discovery
This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively.
Bayesian Neural Architecture Search using A Training-Free Performance Metric
Recurrent neural networks (RNNs) are a powerful approach for time series prediction.
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns.
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task.
Equivariant quantum circuits for learning on weighted graphs
When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.
A Modular Hybridization of Particle Swarm Optimization and Differential Evolution
In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance.
Local Subspace-Based Outlier Detection using Global Neighbourhoods
In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components.
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.
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions.
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e. g., automated selection of optimization algorithms.
Artificial Intelligence and Data Science in the Automotive Industry
Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future.
Cluster-based Kriging Approximation Algorithms for Complexity Reduction
In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework.
Evolving the Structure of Evolution Strategies
Various variants of the well known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) have been proposed recently, which improve the empirical performance of the original algorithm by structural modifications.
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance.
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator.
Prototype Discovery using Quality-Diversity
An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions.
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes
Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics.
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
We introduce in this work a simple way to interpolate between the random global search of EAs and their deterministic counterparts which sample from a fixed radius only.
Online Selection of CMA-ES Variants
An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems.
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning
We show on a set of high-conditioning functions that online whitening tackles SACOBRA's early stagnation issue and reduces the optimization error by a factor between 10 to 1e12 as compared to the plain SACOBRA, though it imposes many extra function evaluations.
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
In this paper, an efficient algorithm for the computation of the exact EHVI for a generic case is proposed.
Modeling User Selection in Quality Diversity
The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize.
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
In this work we compare sequential and integrated algorithm selection and configuration approaches for the case of selecting and tuning the best out of 4608 variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) tested on the Black Box Optimization Benchmark (BBOB) suite.
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems.
On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks
Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem.
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP).
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones
Given a point in $m$-dimensional objective space, any $\varepsilon$-ball of a point can be partitioned into the incomparable, the dominated and dominating region.
Differential evolution outside the box
A wide range of popular Differential Evolution configurations is considered in this study.
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost.
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability
Moreover, we observe that the ``fast'' mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover.
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case
One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem.
Neural Network Design: Learning from Neural Architecture Search
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests.
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
We empirically observe that the initial F1 test score jumps from 0. 29 to 0. 95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19.
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i. e. parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure.
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process.
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
In this work we propose a weight soft-regularization method based on the Oblique manifold.
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated.
Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling
Within machine learning model evaluation regimes, feature selection is a technique to reduce model complexity and improve model performance in regards to generalization, model fit, and accuracy of prediction.
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants.
An Analysis of Phenotypic Diversity in Multi-Solution Optimization
More and more, optimization methods are used to find diverse solution sets.
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.
Designing Air Flow with Surrogate-assisted Phenotypic Niching
In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself.
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.
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.
Deep Multiagent Reinforcement Learning: Challenges and Directions
This paper surveys the field of deep multiagent reinforcement learning.
Lifelong Computing
When detecting anomalies, novelties, new goals or constraints, a lifelong computing system activates an evolutionary self-learning engine that runs online experiments to determine how the computing-learning system needs to evolve to deal with the changes, thereby changing its architecture and integrating new computing elements from computing warehouses as needed.
Non-Elitist Selection Can Improve the Performance of Irace
In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines
Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation.
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies
In this work, we build on the recent study of Vermetten et al. [GECCO 2020], who presented a data-driven approach to investigate promising switches between pairs of algorithms for numerical black-box optimization.
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
Particularly, we show that the number of runs used in many benchmarking studies, e. g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks.
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a so-called quantum neural network -- to serve as a basis for a machine learning model.
Quantum-Enhanced Selection Operators for Evolutionary Algorithms
We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization.
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications
More specifically, in this paper, a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming.
Reinforcement Learning Assisted Recursive QAOA
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems.
Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space.
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection.
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.
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.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
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.
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.
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.
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.
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima.
Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis
The potential of SA-ALAE is shown by generating engineering blueprints in a real automotive design task.
Markus Thill Temporal convolutional autoencoder for unsupervised anomaly detection in time series
Learning temporal patterns in time series remains a challenging task up until today.
On the Noise Scheduling for Generating Plausible Designs with Diffusion Models
We delve into the impact of noise schedules of diffusion models on the plausibility of the outcome: there exists a range of noise levels at which the model's performance decides the result plausibility.
MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts
Choosing a set of benchmark problems is often a key component of any empirical evaluation of iterative optimization heuristics.
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.
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification.
A Functional Analysis Approach to Symbolic Regression
The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.
Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks
Although Deep Reinforcement Learning (DRL) methods can learn effective policies for challenging problems such as Atari games and robotics tasks, algorithms are complex and training times are often long.
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.
Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder
As such, FID might not be suitable to assess the performance of DGMs for a generative design task.
