Abstract
Unsupervised learning is an important component in many recent successes in machine learning. The autoencoder neural network is one of the most prominent approaches to unsupervised learning. Here, we use the genetic programming paradigm to create autoencoders and find that the task is difficult for genetic programming, even on small datasets which are easy for neural networks. We investigate which aspects of the autoencoding task are difficult for genetic programming.
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CITATION STYLE
McDermott, J. (2019). Why Is Auto-Encoding Difficult for Genetic Programming? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11451 LNCS, pp. 131–145). Springer Verlag. https://doi.org/10.1007/978-3-030-16670-0_9
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