A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8391-9_32
Reference24 articles.
1. Bakurov, I., Castelli, M., Fontanella, F., di Freca, A.S., Vanneschi, L.: A novel binary classification approach based on geometric semantic genetic programming. Swarm Evol. Comput. 69, 101028 (2022). https://doi.org/10.1016/j.swevo.2021.101028
2. Beadle, L., Johnson, C.G.: Semantically driven crossover in genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 111–116 (2008)
3. Beadle, L., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genet. Program Evolvable Mach. 10, 307–337 (2009)
4. Beadle, L., Johnson, C.G.: Semantically driven mutation in genetic programming. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1336–1342 (2009)
5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
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