Abstract
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.
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Notes
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d1291, u2152, usa23505 and d18512 are benchmarks from the well-known TSPLIB. The remaining instances are benchmarks from real-life geographical data; these are wi29, dj38, qa194, zi929, ca4663, ym7663, ja9874, gr9882, sw24978. All these instances can be found at http://www.math.uwaterloo.ca/tsp/world/countries.html and http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html.
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Acknowledgements
The N8 HPC computer cluster used to host our evolutionary cross-domain hyper-heuristics and test their performance was provided and funded by the N8 consortium and EPSRC (Grant No.EP/K000225/1). The Centre is co-ordinated by the Universities of Leeds and Manchester.
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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: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_19
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