Publisher
Springer Nature Switzerland
Reference31 articles.
1. Alonso, C.L., Montaña, J.L., Borges, C.E.: Evolution strategies for constants optimization in genetic programming. In: 2009 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 703–707 (2009). https://doi.org/10.1109/ICTAI.2009.35
2. Athanasios Tsanas, A.X.: Energy efficiency (2012). https://doi.org/10.24432/C51307
3. Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis. J. Mach. Learn. Res. 18(1), 2653–2688 (2017)
4. Bouter, A., Alderliesten, T., Witteveen, C., Bosman, P.A.N.: Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), pp. 705–712. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3071178.3071272
5. Cerny, B.M., Nelson, P.C., Zhou, C.: Using differential evolution for symbolic regression and numerical constant creation. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO08). ACM (2008). https://doi.org/10.1145/1389095.1389331