Using Cartesian Genetic Programming Approach with New Crossover Technique to Design Convolutional Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8311

@Article{torabi:NPL,

• author = "Ali Torabi and Arash Sharifi and Mohammad Teshnehlab",
• title = "Using Cartesian Genetic Programming Approach with New Crossover Technique to Design Convolutional Neural Networks",
• journal = "Neural Processing Letters",
• year = "2023",
• volume = "55",
• pages = "5451--5471",
• month = oct,
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN, Convolutional neural network, Neural architecture search, Crossover, Multiple sequence alignment",
• ISSN = "1370-4621",
• URL = "https://rdcu.be/ddL4Z",
• URL = "http://link.springer.com/article/10.1007/s11063-022-11093-0",
• DOI = "doi:10.1007/s11063-022-11093-0",
• size = "21 pages",
• abstract = "In image classification problems, Convolutional Neural Networks (CNNs) are deep neural networks that include a variety of different layers aimed at classifying images. Until today, the most promising and state-of-the-art method in image recognition tasks is CNN. Tuning the deep network with a large number of hyperparameters to maximize performance would be an excruciating task that requires lots of time and engineering efforts. To construct that high-performance architecture, experts should go through a lot of trial and error. Neural Architecture Search is a way to automatically fabricate an accurate network architecture. An evolutionary algorithm called Cartesian Genetic Programming (CGP) with a new crossover operation based on the multiple Sequence Alignment algorithm is proposed in this paper to construct an appropriate neural network without the burden of building manually. This new method has a remarkable improvement over a standard CGP only by adding a crossover operator. The datasets for training on the proposed method were CIFAR-10 and CIFAR-100. The results show that it achieves a good balance between accuracy and the number of trainable parameters compared to the other state-of-the-art methods",
• notes = "Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran",

}

Genetic Programming entries for Ali Torabi Arash Sharifi Mohammad Teshnehlab

Citations