Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56957-9_2
Reference32 articles.
1. Błądek, I., Krawiec, K.: Solving symbolic regression problems with formal constraints. In: GECCO 2019: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 977–984. ACM, Prague (2019). https://doi.org/10.1145/3321707.3321743
2. Błądek, I., Krawiec, K.: Counterexample-driven genetic programming for symbolic regression with formal constraints. IEEE Trans. Evol. Comput. 27(5), 1327–1339 (2023). https://doi.org/10.1109/TEVC.2022.3205286
3. Błądek, I., Krawiec, K., Swan, J.: Counterexample-driven genetic programming: heuristic program synthesis from formal specifications. Evol. Comput. 26(3), 441–469 (2018). https://doi.org/10.1162/evco_a_00228
4. Boldi, R., et al.: The problem solving benefits of down-sampling vary by selection scheme. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 527–530 (2023)
5. Boldi, R., et al.: Informed down-sampled lexicase selection: identifying productive training cases for efficient problem solving (2023). https://doi.org/10.48550/arXiv.2301.01488, arXiv:2301.01488
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