- author = "Elizaveta Ivanchik and Alexander Hvatov",
- title = "Directed differential equation discovery using modified mutation and cross-over operators",
- booktitle = "2023 IEEE Congress on Evolutionary Computation (CEC)",
- year = "2023",
- editor = "Gui DeSouza and Gary Yen",
- address = "Chicago, USA",
- month = "1-5 " # jul,
- keywords = "genetic algorithms, genetic programming, equation discovery, evolutionary algorithm, knowledge-based algorithm, directed evolution",
- isbn13 = "979-8-3503-1458-8/23/",
-
URL = "
https://human-competitive.org/sites/default/files/entry_hvatov.txt",
-
URL = "
https://human-competitive.org/sites/default/files/directed_differential_equation_discovery_using_modified_mutation_and_cross-over_operators.pdf",
-
DOI = "
doi:10.1109/CEC53210.2023.10254047",
-
code_url = "https://github.com/ITMO-NSS-team/CEC_2023_knowledge",
- size = "10 pages",
- abstract = "The discovery of equations with knowledge of the process origin is a tempting prospect. However, most equation discovery tools rely on gradient methods, which offer limited control over parameters. An alternative approach is the evolutionary equation discovery, which allows modification of almost every optimization stage. we examine the modifications that can be introduced into the evolutionary operators of the equation discovery algorithm, taking inspiration from directed evolution techniques employed in fields such as chemistry and biology. The resulting approach, dubbed directed equation discovery, demonstrates a greater ability to converge towards accurate solutions than the conventional method. To support our findings, we present experiments based on Burgers wave, and Korteweg-de Vries equations.",
-
notes = "Entered 2023 HUMIES
CEC2023",
