Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection

2026-05-22 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Quantum Computing · Depth: Expert, extended

Summary

The Quantum Genetic Negative Selection Algorithm (QGNSA) is a novel approach integrating a Quantum Genetic Algorithm (QGA) into the EvoSeedRNSA framework to improve anomaly detection. This method leverages quantum superposition and probabilistic amplitude adjustment to enhance search space exploration and convergence during detector generation. Empirical evaluations on the Metaverse Financial Transactions Dataset, comprising 78,600 records with 12 features after preprocessing, demonstrated QGNSA's superior anomaly detection recall and lower false negative rates compared to its classical counterpart. However, QGNSA exhibited an increased false positive rate. The classical EvoSeedRNSA, conversely, showed higher specificity and overall accuracy but struggled with detecting actual anomalies. The study involved 25 runs per algorithm, using parameters like 10 generations, population size 10, and a threshold of 1.6, with QGNSA requiring 192 qubits for a precision of 16.

Key takeaway

For Machine Learning Engineers developing anomaly detection systems for high-dimensional data, consider Quantum Genetic Negative Selection Algorithms (QGNSA). If your application prioritizes detecting all anomalies (high recall), QGNSA offers superior performance over classical methods, though you may experience more false positives. You should carefully tune parameters like the detection threshold and qubit precision to balance recall and specificity based on your specific domain's risk tolerance.

Key insights

Quantum Genetic Algorithms can enhance Negative Selection Algorithms for anomaly detection by improving recall.

Principles

• Quantum superposition aids broader solution space exploration.
• Probabilistic amplitude adjustment guides convergence in QGAs.
• Anomaly detection priorities dictate algorithm parameter tuning.

Method

QGNSA replaces EvoSeedRNSA's classical genetic algorithm with a QGA. It initializes a quantum circuit, measures it to generate detectors, evaluates fitness, and adjusts qubit rotation angles based on the best detector until an optimal fitness or MaxGen is reached.

In practice

• Use QGNSA for high-recall anomaly detection tasks like cybersecurity.
• Adjust QGNSA threshold to balance recall and specificity needs.
• Consider qubit requirements (e.g., 192 for 12 features, precision 16) for hardware deployment.

Topics

• Quantum Computing
• Anomaly Detection
• Negative Selection Algorithms
• Quantum Genetic Algorithms
• EvoSeedRNSA
• Metaverse Financial Transactions Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.