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arXiv Machine Learning By Dominik Jehle, Tom Zehle

Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization

Read the original on arXiv Machine Learning →

The paper introduces a Bayesian Evolutionary Optimization framework for automatically discovering task‑specific multi‑agent system (MAS) topologies. It combines evolutionary operators that generate diverse workflow graphs with a graph‑aware Gaussian Process and Expected Improvement to guide costly evaluations toward promising candidates. Experiments on BigCodeBench for code generation and on tabular preprocessing benchmarks show that the evolved MAS architectures outperform other evaluated agent systems and improve performance on temporal datasets while remaining competitive on IID data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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