50 citations · 96 across the 16 of their papers we have counts for
16 papers
ROIDS: Robust Outlier-Aware Informed Down-Sampling
Alina Geiger, Martin Briesch, Dominik Sobania +1
Informed down-sampling (IDS) is known to improve performance in symbolic regression when combined with various selection strategies, especially tournament selection. However, recen…
Transformer Semantic Genetic Programming for d-dimensional Symbolic Regression Problems
Philipp Anthes, Dominik Sobania, Franz Rothlauf
Transformer Semantic Genetic Programming (TSGP) is a semantic search approach that uses a pre-trained transformer model as a variation operator to generate offspring programs with…
Was Tournament Selection All We Ever Needed? A Critical Reflection on Lexicase Selection
Alina Geiger, Martin Briesch, Dominik Sobania +1
The success of lexicase selection has led to various extensions, including its combination with down-sampling, which further increased performance. However, recent work found that…
Transformer Semantic Genetic Programming for Symbolic Regression
Philipp Anthes, Dominik Sobania, Franz Rothlauf
In standard genetic programming (stdGP), solutions are varied by modifying their syntax, with uncertain effects on their semantics. Geometric-semantic genetic programming (GSGP), a…
ComfyGI: Automatic Improvement of Image Generation Workflows
Dominik Sobania, Martin Briesch, Franz Rothlauf
Automatic image generation is no longer just of interest to researchers, but also to practitioners. However, current models are sensitive to the settings used and automatic optimiz…
A Performance Analysis of Lexicase-Based and Traditional Selection Methods in GP for Symbolic Regression
Alina Geiger, Dominik Sobania, Franz Rothlauf
In recent years, several new lexicase-based selection variants have emerged due to the success of standard lexicase selection in various application domains. For symbolic regressio…
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop
Martin Briesch, Dominik Sobania, Franz Rothlauf
Large Language Models (LLM) are already widely used to generate content for a variety of online platforms. As we are not able to safely distinguish LLM-generated content from human…
Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content
Martin Huschens, Martin Briesch, Dominik Sobania +1
This paper examines how individuals perceive the credibility of content originating from human authors versus content generated by large language models, like the GPT language mode…
Untangling the Effects of Down-Sampling and Selection in Genetic Programming
Ryan Boldi, Ashley Bao, Martin Briesch +4
Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection, which is often computationally expensive. Down-sampling trai…
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems
Alina Geiger, Dominik Sobania, Franz Rothlauf
Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of rand…
MTGP: Combining Metamorphic Testing and Genetic Programming
Dominik Sobania, Martin Briesch, Philipp Röchner +1
Genetic programming is an evolutionary approach known for its performance in program synthesis. However, it is not yet mature enough for a practical use in real-world software deve…
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
Dominik Sobania, Martin Briesch, Carol Hanna +1
To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually e…
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
Ryan Boldi, Martin Briesch, Dominik Sobania +5
Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection…
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming
Dominik Sobania, Martin Briesch, Franz Rothlauf
GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for sof…
Recent Developments in Program Synthesis with Evolutionary Algorithms
Dominik Sobania, Dirk Schweim, Franz Rothlauf
The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques n…
The Randomness of Input Data Spaces is an A Priori Predictor for Generalization
Martin Briesch, Dominik Sobania, Franz Rothlauf
Over-parameterized models can perfectly learn various types of data distributions, however, generalization error is usually lower for real data in comparison to artificial data. Th…