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Found 103 papers, 29 papers with code
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic structures.
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms
However, in some cases they fail to generate competitive algorithms or the code optimization stalls, and we are left with no recourse because of a lack of understanding of the generation process and generated codes.
A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for High-dimension Shape Generation
This approach efficiently encodes complex meshes into continuous implicit representations, such as encoding a 15k-vertex mesh to a 512-dimensional latent code without learning.
Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions
This survey addresses this gap by providing a comprehensive review of diffusion models for tabular data.
Abnormal Mutations: Evolution Strategies Don't Require Gaussianity
The mutation process in evolution strategies has been interlinked with the normal distribution since its inception.
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
Variational quantum algorithms, such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing Noisy Intermediate-Scale Quantum devices to address challenging combinatorial optimization tasks like the maximum cut problem.
Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations
Surrogate models provide efficient alternatives to computationally demanding real-world processes but often require large datasets for effective training.
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks
Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes.
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms.
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
Experiments using GPT-3. 5-turbo and GPT-4o models demonstrate that GPT-3. 5-turbo fails to adhere to the specific mutation instructions, while GPT-4o is able to adapt its mutation based on the prompt engineered dynamic prompts.
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for 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.
NeuroNURBS: Learning Efficient Surface Representations for 3D Solids
Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD).
Comparative Analysis of Indicators for Multiobjective Diversity Optimization
Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions.
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks.
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
We apply the log-normal method to the attack of fake detectors, and get successful attacks: importantly, these attacks are not detected by detectors specialized on classical adversarial attacks.
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance.
Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification
Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.
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.
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.
LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms.
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem
Our experiments show that our approach is highly successful in solving the instability issue of the performance ratios and leads to evolving reliable sets of chance constraints with significantly different performance for various types of algorithms.
Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration
The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning.
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.
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
Optimization problems in dynamic environments have recently been the source of several theoretical studies.
Fréchet Denoised Distance: 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.
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.
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.
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.
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.
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-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.
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-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.
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.
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.
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.
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.
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.
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.
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Deep Multiagent Reinforcement Learning: Challenges and Directions
This paper surveys the field of deep multiagent reinforcement learning.
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.
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.
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.
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.
An Analysis of Phenotypic Diversity in Multi-Solution Optimization
More and more, optimization methods are used to find diverse solution sets.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Differential evolution outside the box
A wide range of popular Differential Evolution configurations is considered in this study.
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.
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).
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.
Bayesian Neural Architecture Search using A Training-Free Performance Metric
Recurrent neural networks (RNNs) are a powerful approach for time series prediction.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
