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Cartesian Genetic Programming: Some New Detections

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Advances in Information and Communication (FICC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 439))

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Abstract

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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Authors and Affiliations

  1. University of Information and Communication Technology - Thai Nguyen University, Thai Nguyen City, Vietnam

    Thuong Pham Thi

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  1. Thuong Pham Thi
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Correspondence to Thuong Pham Thi .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Thi, T.P. (2022). Cartesian Genetic Programming: Some New Detections. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-030-98015-3_20

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