- author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson",
- title = "Genetic Improvement of Shoreline Evolution Forecasting Models",
- booktitle = "GI @ GECCO 2022",
- year = "2022",
- editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and W. B. Langdon and Justyna Petke",
- pages = "1916--1923",
- address = "Boston, USA",
- publisher_address = "New York, NY, USA",
- month = "9 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- keywords = "genetic algorithms, genetic programming, genetic improvement, Cartesian Genetic Programming, RCGP, symbolic regression, forecasting, shoreline evolution, earth observation salellite, CGP-ShoreFor, ShorefFor, physical sciences, geography, coastal erosion, Tairua New Zealand, Mielke correlation coefficient",
- isbn13 = "978-1-4503-9268-6/22/07",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/AlNajar_2022_GI.pdf",
-
DOI = "
10.1145/3520304.3534041",
-
slides_url = "https://sourcesup.renater.fr/wiki/atelieromp/_media/presentation_envia_mahmoud_al_ajar_omp_v2.pdf",
-
slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/najar-genetic-improvement-of-shoreline-evolution-forecasting-gi-gecco-22.pdf",
-
video_url = "
https://www.youtube.com/watch?v=66UiDk9lsnc&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=6",
- size = "8+1 pages",
- abstract = "Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) that has been successfully used in a large variety of tasks including data-driven symbolic regression. We formulate the problem of shoreline evolution forecasting as a Genetic Improvement (GI) problem using CGP to encode and improve upon ShoreFor, an equilibrium shoreline prediction model, to study the effectiveness of CGP in GI in forecasting tasks. This work presents an empirical study of the sensitivity of CGP to a number of evolutionary configurations and constraints and compares the performances of the evolved models to the base ShoreFor model.",
-
notes = "Supplementary Material: Figure 8: Example evolved
CGP-ShoreFor model graphs
15 years (dauly sampling).
Goldman mutation \cite{goldman:2013:EuroGP}
http://geneticimprovementofsoftware.com/events/gecco2022
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
