- author = "Manuel Lopez-Ibanez",
- title = "{ACM SIGEVO} Best Dissertation Award 2021",
- journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation",
- year = "2021",
- volume = "14",
- number = "3",
- pages = "5--7",
- month = oct,
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1931-8499",
-
URL = "
https://evolution.sigevo.org/issues/HTML/sigevolution-14-3/home.html",
-
URL = "https://dl.acm.org/citation.cfm?id=3357514",
-
DOI = "
doi:10.1145/3490676.3490677",
-
abstract = "Winner \cite{DBLP:phd/basesearch/Virgolin20}
This dissertation introduces significant innovations in model-based genetic programming (GP) enabling the efficient discovery of compact and accurate solutions, which is key for supporting explainable Artificial Intelligence. In collaboration with medical researchers, the proposed innovations are applied successfully to improve historical 3D-dose reconstruction in radiation oncology which is used to design more effective treatments for cancer patients. This work is a stellar example of the positive real-world impact of Evolutionary Computation.
https://sig.sigevo.org/index.html/SIGEVO+Dissertation+Award",
- notes = "University of Malaga, Spain",
