Scopus; WoS

Cartesian Genetic Programming: Some New Detections

Năm XB 2022 Tạp chí / Hội thảo Lecture Notes in Networks and Systems Volume 439 LNNS DOI / Link https://doi.org/10.1007/978-3-030-98015-3_20 ↗

Tác giả

Pham Thi Thuong

Tóm tắt

In this paper, we propose a variant of the fitness function for Cartesian Genetic Programming, called CGP-nMean. Besides, we also analyze systematically the dependence of this method on its experimental parameters to find the relationship between them and to define what is the best configuration. In order to evaluate the effectiveness of the proposed approach, we run experiments on eighteen symbolic regression problems. The experimental results show that (1) the generalizability of the learned model by CGP-nMean is significantly better than that of standard CGP on the most of tested problems; (2) the performance of CGP is significantly affected by experimental parameters, and controlling these parameters will give us the best configuration of CGP-nMean (CGP-nMean(Best)).

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