EVOTER: Evolution of Transparent Explainable Rule sets

Created by W.Langdon from gp-bibliography.bib Revision:1.8506

@InProceedings{shahrzad:2025:GECCOcomp,

• author = "Hormoz Shahrzad and Babak Hodjat and Risto Miikkulainen",
• title = "{EVOTER:} Evolution of Transparent Explainable Rule sets",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference: Hot off the Press",
• year = "2025",
• editor = "Eric Medvet",
• pages = "67--68",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, rule set evolution, explainable AI, XAI",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734238",
• DOI = "doi:10.1145/3712255.3734238",
• size = "2 pages",
• abstract = "EVOTER is a framework for evolving transparent, interpretable rule sets that combine predictive accuracy with human readability. Unlike post-hoc explainers, EVOTER constructs intrinsically interpretable models through list-based grammatical evolution. The framework introduces three innovations: time-lag operators, feature-feature comparisons, and nonlinear transformations. It demonstrates strong performance across diverse domains including classification, time-series forecasting, and policy learning. By enabling models that are both effective and auditable, EVOTER advances the state of explainable artificial intelligence.",
• notes = "GECCO-2025 A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Hormoz Shahrzad Babak Hodjat Risto Miikkulainen

Citations