Evolutionary algorithms have been widely used to optimise or design search algorithms, however, very few have considered evolving iterative algorithms. In this paper, we introduce a novel extension to Cartesian Genetic Programming that allows it to encode iterative algorithms. We apply this technique to the Traveling Salesman Problem to produce human-readable solvers which can be then be independently implemented. Our experimental results demonstrate that the evolved solvers scale well to much larger TSP instances than those used for training.
CITATION STYLE
Ryser-Welch, P., Miller, J. F., Swan, J., & Trefzer, M. A. (2016). Iterative cartesian genetic programming: Creating general algorithms for solving travelling salesman problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9594, pp. 294–310). Springer Verlag. https://doi.org/10.1007/978-3-319-30668-1_19
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