@inproceedings{a5bbefdd4e314d829d9345d978790c50,
title = "Automatically Evolving Lookup Tables for Function Approximation",
abstract = "Many functions, such as square root, are approximated and sped up with lookup tables containing pre-calculated values. We introduce an approach using genetic algorithms to evolve such lookup tables for any smooth function. It provides double precision and calculates most values to the closest bit, and outperforms reference implementations in most cases with competitive run-time performance.",
keywords = "Covariance matrix adaptation, Genetic Improvement, Objective function",
author = "Oliver Krauss and Langdon, {William B.}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020 ; Conference date: 15-04-2020 Through 17-04-2020",
year = "2020",
doi = "10.1007/978-3-030-44094-7_6",
language = "English",
isbn = "9783030440930",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "84--100",
editor = "Ting Hu and Nuno Louren{\c c}o and Eric Medvet and Federico Divina",
booktitle = "Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings",
address = "Germany",
}