Abstract
Genetic Programming (GP) is an evolutionary methodology for generating programs typically applied to classification and symbolic regression problems. GP is not ordinarily applied directly to solve combinatorial optimisation problems. However, GP can be considered similar to hyper-heuristic methods which apply simple heuristics sequentially to a given solution to a problem, a set of operations or a program. Consequently, this paper will present a novel implementation of GP which can directly solve optimisation problems. Similar to hyper-heuristics, a hill-climbing method to GP is presented whereby programs are constructed in small parts or phases, Phased-GP. Furthermore, acceptance strategies for the use of Phased-GP are explored to improve its performance. When Phased-GP is applied directly to Traveling Salesman Problems of up to 1000 cites solutions within 6% of optimal can be derived using only simple operators, a significant improvement over standard GP.
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References
Applegate, D., Cook, W., Rohe, A.: Chained Lin-Kernighan for large traveling salesman problems. Informs J. Comput. 15(1), 82–92 (2003)
Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5(1), 17–26 (2001)
Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)
Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: International Conference on the Practice and Theory of Automated Timetabling, pp. 176–190. Springer (2000)
Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6(6), 791–812 (1958)
Dimopoulos, C., Zalzala, A.M.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv. Eng. Softw. 32(6), 489–498 (2001)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
Duflo, G., Kieffer, E., Brust, M.R., Danoy, G., Bouvry, P.: A GP hyper-heuristic approach for generating TSP heuristics. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 521–529. IEEE (2019)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U Michigan Press (1975)
Keller, R.E., Poli, R.: Linear genetic programming of parsimonious metaheuristics. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4508–4515. IEEE (2007)
Kheiri, A., Keedwell, E.: A hidden markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems. Evol. Comput. 25(3), 473–501 (2017)
Koza, J.R.: Genetic Programming (1992)
Koza, J.R.: Genetic programming II: Automatic Discovery of Reusable Programs. MIT press (1994)
Nguyen, S., Zhang, M., Johnston, M.: A genetic programming based hyper-heuristic approach for combinatorial optimisation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1299–1306 (2011)
Oltean, M.: Evolving evolutionary algorithms using linear genetic programming. Evol. Comput. 13(3), 387–410 (2005)
Ryser-Welch, P., Miller, J.F., Swan, J., Trefzer, M.A.: Iterative cartesian genetic programming: creating general algorithms for solving travelling salesman problems. In: Genetic Programming: 19th European Conference, EuroGP 2016, Porto, Portugal, March 30-April 1, 2016, Proceedings 19, pp. 294–310. Springer (2016)
Soh, C.K., Yang, Y.: Genetic programming-based approach for structural optimization. J. Comput. Civ. Eng. 14(1), 31–37 (2000)
Tavares, J., Pereira, F.B.: Designing pheromone update strategies with strongly typed genetic programming. In: Genetic Programming: 14th European Conference, EuroGP 2011, Torino, Italy, April 27–29, 2011. Proceedings 14, pp. 85–96. Springer (2011)
Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput. Indus. Eng. 54(3), 453–473 (2008)
Tinós, R., Whitley, D., Ochoa, G.: A new generalized partition crossover for the traveling salesman problem: tunneling between local optima. Evol. Comput. 28(2), 255–288 (2020)
Whitley, D., Hains, D., Howe, A.: Tunneling between optima: partition crossover for the traveling salesman problem. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 915–922 (2009)
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Chitty, D.M. (2024). Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_25
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