Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Baeck, Thomas"'
Autor:
Long, Fu Xing, Frenzel, Moritz, Krause, Peter, Gitterle, Markus, Bäck, Thomas, van Stein, Niki
In landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly genera
Externí odkaz:
http://arxiv.org/abs/2409.01446
Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art approa
Externí odkaz:
http://arxiv.org/abs/2408.03747
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling o
Externí odkaz:
http://arxiv.org/abs/2407.06849
Autor:
Kalkreuth, Roman, Baeck, Thomas
The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language. C inherently follows a procedural programming paradigm, which entails challenges in providing a reusable and scalable implementation model
Externí odkaz:
http://arxiv.org/abs/2406.09038
Autor:
van Stein, Niki, Bäck, Thomas
Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT
Externí odkaz:
http://arxiv.org/abs/2405.20132
Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterative search algorithms and stud
Externí odkaz:
http://arxiv.org/abs/2405.18772
Autor:
Internò, Christian, Raponi, Elena, van Stein, Niki, Bäck, Thomas, Olhofer, Markus, Jin, Yaochu, Hammer, Barbara
Federated learning (FL) represents a pivotal shift in machine learning (ML) as it enables collaborative training of local ML models coordinated by a central aggregator, all without the need to exchange local data. However, its application on edge dev
Externí odkaz:
http://arxiv.org/abs/2405.10271
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
Externí odkaz:
http://arxiv.org/abs/2405.01226
Optimization problems in dynamic environments have recently been the source of several theoretical studies. One of these problems is the monotonic Dynamic Binary Value problem, which theoretically has high discriminatory power between different Genet
Externí odkaz:
http://arxiv.org/abs/2404.15837
A great interest has arisen in using Deep Generative Models (DGM) for generative design. When assessing the quality of the generated designs, human designers focus more on structural plausibility, e.g., no missing component, rather than visual artifa
Externí odkaz:
http://arxiv.org/abs/2403.05352