CANTS-GP: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.9105

@InProceedings{Desell:2025:GPTP,

• author = "AbdElRahman A. ElSaid and Travis Desell",
• title = "{CANTS-GP}: A Nature-Inspired Metaheuristic for Graph Based Genetic Programs",
• booktitle = "Genetic Programming Theory and Practice XXII",
• year = "2025",
• editor = "Bogdan Burlacu and Fabricio {Olivetti de Franca} and Alexander Lalejini and Stephen Kelly and Wolfgang Banzhaf",
• series = "Genetic and Evolutionary Computation",
• pages = "103--125",
• address = "Michigan State University, USA",
• month = jun # " 5-7",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, XAI",
• isbn13 = "978-981-95-6397-5",
• DOI = "10.1007/978-981-95-6398-2_6",
• abstract = "... which is a nature-inspired metaheuristic that constructs computational graphs with trainable arithmetic functions rather than opaque neural units. CANTS-GP employs a multi-colony framework: simulated ant agents traverse an unbounded continuous search space guided by pheromone trails, and colonies periodically exchange evolving parameters pheromone evaporation rates and the number of foraging ants via a particle swarm optimization-inspired information sharing. Ant-generated paths are consolidated into flexible, acyclic computational graphs through the DBSCAN clustering algorithm and depth-first search-based cycle removal. Key innovations include adaptive colony evaporation control, dynamic movement heuristics, and multi-colony evolutionary strategies. We evaluate CANTS-GP on six benchmark time-series forecasting tasks, demonstrating consistent performance gains over state-of-the-art methods while yielding transparent models whose arithmetic structures provide model interpretability.",
• notes = "published in 2026 after the workshop",

}

Genetic Programming entries for Ahmed ElSaid Travis Desell

Citations