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Machine Learning, Evolutionary Algorithms & Interpretability
机器学习 · 进化算法 · 可解释性
Core contributions: an ML-enabled two-stage stochastic programming framework with learned scenario reduction; a parallel compact cuckoo search for 3D trajectory optimization; a Simplified Phasmatodea population evolution algorithm; and a concept-level interpretable SOM for visual analysis of high-dimensional Pareto fronts.
Related Publications
- Journal · 2025 J. S. Pan, P. C. Song, S. C. Chu, V. Snášel, J. Watada. Machine learning-enabled evolutionary two-stage stochastic programming, IEEE Trans. Emerging Topics in Computational Intelligence, early access, 2025.
- SCI · 2020 P. C. Song, J. S. Pan, S. C. Chu. A parallel compact cuckoo search algorithm for three-dimensional path planning, Applied Soft Computing, vol. 94, Art. no. 106443, 2020.
- SCI · 2022 P. C. Song, S. C. Chu, J. S. Pan, H. Yang. Simplified Phasmatodea population evolution algorithm for optimization, Complex & Intelligent Systems, vol. 8(4), pp. 2749–2767, 2022.
- CCF-C · 2024 P. C. Song, J. S. Pan, X. Sun, S. C. Chu. Concept-level interpretable SOM for visual analysis of high-dimensional non-dominated solution set, ICIC 2024, pp. 27–38, 2024.