Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks

• URL: http://arxiv.org/abs/2410.00129v2
• Date: Sun, 13 Oct 2024 23:43:33 GMT
• Title: Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks
• Authors: Maciej Krzywda, Szymon Łukasik, Amir Gandomi H,
• Abstract summary: In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. In this work, we use pure Genetic Programming Approach to design CNNs, which employs only one genetic operation. In the course of preliminary experiments, our methodology yields promising results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In this work, we use pure Genetic Programming Approach to design CNNs, which employs only one genetic operation, i.e., mutation. In the course of preliminary experiments, our methodology yields promising results.

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