24 citations · 44 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples
Malte Probst, Franz Rothlauf
We propose an alternative method for training a classification model. Using the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is possible to reach a classif…
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Malte Probst, Franz Rothlauf
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural n…
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
Malte Probst, Franz Rothlauf, Jörn Grahl
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative ne…
Problem Complexity in Parallel Problem Solving
Sebastian Herrmann, Jörn Grahl, Franz Rothlauf
Recent works examine the relationship between the communication structure and the performance of a group in a problem solving task. Some conclude that inefficient communication net…