Diverse Counterfactual Explanations by Differential Evolution with Ablation Strategies for Uncertain Capacitated Arc Routing Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{wang:2025:CEC6,

• author = "Shaolin Wang and Haoyang Che and He Jiang and Yi Mei and Yi Liu2 and Ying Gu",
• title = "Diverse Counterfactual Explanations by Differential Evolution with Ablation Strategies for Uncertain Capacitated Arc Routing Problem",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Uncertainty, Diversity reception, Decision making, Evolutionary computation, Diversity methods, Routing, Robustness, Dynamic programming, Optimization",
• isbn13 = "979-8-3315-3432-5",
• DOI = "10.1109/CEC65147.2025.11043113",
• abstract = "The Uncertain Capacitated Arc Routing Problem (UCARP) presents unique challenges in real-world applications such as waste collection and winter gritting, where task demands and service costs are stochastic. Although Genetic Programming Hyper-Heuristics (GPHH) have demonstrated strong adaptability to such uncertainties by evolving dynamic routing policies, their complex decision-making processes hinder interpretability. To address this, a novel framework called Differential Evolution with Random Ablation (DERA) is introduced to generate diverse and feasible counterfactual explanations. Unlike traditional methods, DERA systematically explores multiple counterfactual scenarios by integrating random ablation into the optimisation process, thereby uncovering a broader range of plausible alternatives. Experimental results across various UCARP instances show that DERA consistently achieves high feasibility, minimal feature changes, and greater diversity in counterfactual explanations compared to baseline methods. This diversity enables a more comprehensive understanding of GPHH-evolved policies, providing actionable insights to improve decision-making transparency and robustness in dynamic environments.",
• notes = "also known as \cite{11043113}",

}

Genetic Programming entries for Shaolin Wang Haoyang Che He Jiang Yi Mei Yi Liu2 Ying Gu

Citations