Neuromemetic Evolutionary Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Liskowski:2020:PPSN,

• author = "Pawel Liskowski and Krzysztof Krawiec and Nihat Engin Toklu",
• title = "Neuromemetic Evolutionary Optimization",
• booktitle = "16th International Conference on Parallel Problem Solving from Nature, Part I",
• year = "2020",
• editor = "Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann",
• volume = "12269",
• series = "LNCS",
• pages = "623--636",
• address = "Leiden, Holland",
• month = "7-9 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, ANN, Optimization, Neural networks, Program synthesis",
• isbn13 = "978-3-030-58111-4",
• URL = "https://doi.org/10.1007/978-3-030-58112-1_43",
• DOI = "doi:10.1007/978-3-030-58112-1_43",
• abstract = "Discrete and combinatorial optimization can be notoriously difficult due to complex and rugged characteristics of the objective function. We address this challenge by mapping the search process to a continuous space using recurrent neural networks. Alongside with an evolutionary run, we learn three mappings: from the original search space to a continuous Cartesian latent space, from that latent space back to the search space, and from the latent space to the search objective. We elicit gradient from that last network and use it to perform moves in the latent space, and apply this Neuromemetic Evolutionary Optimization (NEO) to evolutionary synthesis of programs. Evaluation on a range of benchmarks suggests that NEO significantly outperforms conventional genetic programming.",
• notes = "PPSN XVI PPSN2020",

}

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec Nihat Engin Toklu

Citations