Search Results for author: Niki van Stein

Found 20 papers, 4 papers with code

Stalling in Space: Attractor Analysis for any Algorithm

no code implementations20 Dec 2024 Sarah L. Thomson, Quentin Renau, Diederick Vermetten, Emma Hart, Niki van Stein, Anna V. Kononova

However, STNs are not typically modelled in such a way that models temporal stalls: that is, a region in the search space where an algorithm fails to find a better solution over a defined period of time.

Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms

no code implementations4 Dec 2024 Haoran Yin, Anna V. Kononova, Thomas Bäck, Niki van Stein

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.

Language Modeling Language Modelling +1

EconoJax: A Fast & Scalable Economic Simulation in Jax

1 code implementation29 Oct 2024 Koen Ponse, Aske Plaat, Niki van Stein, Thomas M. Moerland

Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning.

reinforcement-learning Reinforcement Learning

In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics

no code implementations7 Oct 2024 Niki van Stein, Diederick Vermetten, Thomas Bäck

Large Language Models (LLMs) have shown great potential in automatically generating and optimizing (meta)heuristics, making them valuable tools in heuristic optimization tasks.

Code Generation Code Search +2

TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification

no code implementations14 Sep 2024 Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein

In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance.

counterfactual Time Series +2

Landscape-Aware Automated Algorithm Configuration using Multi-output Mixed Regression and Classification

no code implementations2 Sep 2024 Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, Niki van Stein

Overall, configurations with better performance can be best identified by using NN models trained on a combination of RGF and MA-BBOB functions.

Benchmarking

Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI

no code implementations17 Jul 2024 Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Azwad Khan, Elena Ranguelova, Niki van Stein

We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models.

A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization

no code implementations8 Jun 2024 Gjorgjina Cenikj, Ana Nikolikj, Gašper Petelin, Niki van Stein, Carola Doerr, Tome Eftimov

The selection of the most appropriate algorithm to solve a given problem instance, known as algorithm selection, is driven by the potential to capitalize on the complementary performance of different algorithms across sets of problem instances.

Representation Learning

LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics

1 code implementation30 May 2024 Niki van Stein, Thomas Bäck

This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms.

Language Modeling Language Modelling +2

Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration

1 code implementation16 May 2024 Christian Internò, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer

The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning.

Computational Efficiency Federated Learning

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories

no code implementations26 Apr 2024 Niki van Stein, Sarah L. Thomson, Anna V. Kononova

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.

Bias Detection

A Functional Analysis Approach to Symbolic Regression

no code implementations9 Feb 2024 Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna V Kononova

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.

Benchmarking regression +1

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification

no code implementations2 Feb 2024 Qi Huang, Wei Chen, Thomas Bäck, Niki van Stein

In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification.

Classification counterfactual +2

Explainable Benchmarking for Iterative Optimization Heuristics

1 code implementation31 Jan 2024 Niki van Stein, Diederick Vermetten, Anna V. Kononova, Thomas Bäck

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.

Benchmarking Evolutionary Algorithms

Clustering-based Domain-Incremental Learning

no code implementations21 Sep 2023 Christiaan Lamers, Rene Vidal, Nabil Belbachir, Niki van Stein, Thomas Baeck, Paris Giampouras

A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task".

Clustering Continual Learning +2

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.

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