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Currently, the Cartesian Genetic Programming
approaches applied to regression problems tackle the evolutive
strategy from a static point of view. They are confident on the
evolving capacity of the genetic algorithm, with less attention
being paid over alternative methods to enhance the
generalization error of the trained models or the convergence
time of the algorithm. On this article, we propose a novel efficient
strategy to train models using Cartesian Genetic Programming at
a faster rate than its basic implementation. This proposal
achieves greater generalization and enhances the error
convergence. Finally, the complete methodology is tested using
the Australian electricity market as a case study.
CitationGiacometto, F., Kampouropoulos, K., Romeral, J. Short-Term Load Forecasting using Cartesian Genetic Programming: an Efficient Evolutive Strategy Case: Australian electricity market. A: Annual Conference of the IEEE Industrial Electronics Society. "Proceedings of 41th Annual Conference on IEEE Industrial Electronics Society (IECON 2015)". Yokohama: 2015, p. 5087-5094.
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