A Hybrid Framework for Healing Semigroups with Machine Learning

2025/09/01 by Sarayu Sirikonda, Sirikonda, Sarayu, Jasper van de Kreeke +1
Computer Science · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Mathematics #Graph Theory and Algorithms #Machine Learning (cs.LG) #Model-Driven Software Engineering Techniques #Rings and Algebras (math.RA)

paper · pdf · doi:10.48550/arxiv.2509.01763

openalex publication_date 2025/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks associativity and invalidates the algebraic structure. Deterministic methods work for small cardinality n and low corruption but degrade rapidly. Our experiments, carried out on Mace4-generated data sets, demonstrate that our hybrid framework achieves higher healing rates than deterministic-only and ML-only baselines. At a corruption percentage of p=15%, our framework healed 95% of semigroups up to cardinality n=6 and 60% at n=10.

Citations

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