- author = "Mikhail Maslyaev and Alexander Hvatov",
- title = "Comparison of Single- and Multi- Objective Optimization Quality for Evolutionary Equation Discovery",
- booktitle = "Proceedings of the Companion Conference on Genetic and Evolutionary Computation",
- year = "2023",
- editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
- pages = "603--606",
- address = "Lisbon, Portugal",
- series = "GECCO '23",
- month = "15-19 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, symbolic regression, dynamic system modeling, interpretable learning, differential equations, sparse regression: Poster",
- isbn13 = "979-8-4007-0120-7/23/07",
-
URL = "
https://human-competitive.org/sites/default/files/entry_hvatov.txt",
-
URL = "
https://human-competitive.org/sites/default/files/comparison_of_single-_and_multi-_objective_optimization_quality_for_evolutionary_equation_discovery.pdf",
-
DOI = "
doi:10.1145/3583133.3590601",
- size = "4 pages",
- abstract = "Evolutionary differential equation discovery proved to be a tool to obtain equations with less a priori assumptions than conventional approaches, such as sparse symbolic regression over the complete possible terms library. The equation discovery field contains two independent directions. The first one is purely mathematical and concerns differentiation, the object of optimization and its relation to the functional spaces and others. The second one is dedicated purely to the optimizatioal problem statement. Both topics are worth investigating to improve the algorithm’s ability to handle experimental data a more artificial intelligence way, without significant pre-processing and a priori knowledge of their nature. we consider the prevalence of either single-objective optimization, which considers only the discrepancy between selected terms in the equation, or multi-objective optimization, which additionally takes into account the complexity of the obtained equation.",
-
notes = "Entered 2023 HUMIES
GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",
