- author = "David Lynch and James McDermott and Michael O'Neill",
- title = "Program Synthesis in a Continuous Space using Grammars and Variational Autoencoders",
- booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part II",
- year = "2020",
- editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann",
- volume = "12270",
- series = "LNCS",
- pages = "33--47",
- address = "Leiden, Holland",
- month = "7-9 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, PSB1",
- isbn13 = "978-3-030-58114-5",
-
DOI = "
doi:10.1007/978-3-030-58115-2_3",
- abstract = "An important but elusive goal of computer scientists is the automatic creation of computer programs given only input and output examples. We present a novel approach to program synthesis based on the combination of grammars, generative neural models, and evolutionary algorithms. Programs are described by sequences of productions sampled from a Backus-Naur form grammar. A sequence-to-sequence Variational Autoencoder (VAE) is trained to embed randomly sampled programs in a continuous space, the VAE encoder maps a sequence of productions (a program) to a point z in the latent space, and the VAE decoder reconstructs the program given z. After the VAE has converged, we can engage the decoder as a generative model that maps locations in the latent space to executable programs. Hence, an Evolutionary Algorithm can be employed to search for a vector z (and its corresponding program) that solves the synthesis task. Experiments on the program synthesis benchmark suite suggest that the proposed approach is competitive with tree-based GP and PushGP. Crucially, code can be synthesised in any programming language.",
-
notes = "
PPSN2020",
