Welcome to the Computational Intelligence Lab at the University of Oldenburg, where we advance research in artificial intelligence (AI). 

AGI: Evolutionary Cognitive Prompting for Enhancing the Capabilities of Language Models

An agent-based evolutionary system iteratively improves mathematical prompts for language models by evolving a single cognitive prompt through mutation and fitness evaluation. Agents evaluate outputs without access to ground truth, stopping the evolution upon finding correct solutions. Applied to the MATH500 benchmark, the approach significantly improves solution rates—from 15% to 85%—demonstrating the ability of evolutionary prompting to boost the reasoning performance of compact models like Llama 3.2.

Paper on IEEE Conference on Artificial Intelligence (Santa Clara 2025)

Computational Biology: Sequence-Based Protein Pocket Prediction via ProtT5 Embeddings and Spatial Sampling

Protein-ligand binding pockets can be identified using ProtT5-based sequence embeddings, trained on known complexes from the PDBBind database. To compensate for the lack of negative examples, a spatial sampling strategy generates non-pocket sequences for supervised training. The method achieves classification accuracies up to 0.90, with SVM models performing best. A complete pocket detection pipeline enables prediction directly from protein structure. A case study on the c-Src kinase demonstrates the practical value of this method for identifying binding sites relevant to drug discovery.

Paper on IEEE Conference on Artificial Intelligence (Santa Clara 2025)

Evolution: Enhancing Evolutionary Algorithms through Meta-Evolution Strategies

A meta-evolutionary strategy dynamically adjusts key hyperparameters of CMA-ES, PSO, and DE by using a (1+1)-ES with Rechenberg’s 1/5-rule. Each optimizer adapts two specific parameters critical to performance while using restarts and dimension-aware budgets to counter stagnation. Results across benchmark functions reveal that algorithm performance is highly problem-dependent: PSO performs well on simple landscapes, CMA-ES on complex ones, and DE shows robust overall fitness. The approach highlights the power of meta-evolution for adaptive hyperparameter control.

Paper on IEEE Conference on Artificial Intelligence (Santa Clara 2025)

AGI: Cognitive Prompting

Cognitive prompting is a novel approach to guide problem-solving in large language models (LLMs) through structured, human-like cognitive operations, such as goal clarification, decomposition, filtering, abstraction, and pattern recognition. By employing systematic, step-by-step reasoning, cognitive prompting enables LLMs to tackle complex, multi-step tasks more efficiently. We introduce three variants: a deterministic sequence of cognitive operations, a self-adaptive variant in which the LLM dynamically selects the sequence of cognitive operations, and a hybrid variant that uses generated correct solutions as few-shot chain-of-thought prompts. Experiments with LLaMA, Gemma~2, and Qwen models in each two sizes on the arithmetic reasoning benchmark GSM8K demonstrate that cognitive prompting significantly improves performance compared to standard question answering.

Link to the paper.
Further papers from the CI Group.

Covid: Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2 ’s main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.

Link to the paper.

Books

Blogposts on Towards Data Science

SPIEGEL Podcast - Moreno+1: »Eine starke KI? Halte ich für wahrscheinlich«

AI professor Oliver Kramer works on algorithms that exhibit “human-like cognitive performance.” He is convinced that machines will become more intelligent than humans. We should think about that.

Link to the podcast.