- author = "Meghana Kshirsagar and Rushikesh Jachak and Purva Chaudhari and Conor Ryan",
- title = "{GEMO}: Grammatical Evolution Memory Optimization System",
- booktitle = "Proceedings of the 12th International Joint Conference on Computational Intelligence, IJCCI 2020",
- year = "2020",
- editor = "Juan Julian Merelo Guervos and Jonathan M. Garibaldi and Christian Wagner and Thomas Baeck and Kurosh Madani and Kevin Warwick",
- pages = "184--191",
- address = "Budapest, Hungary",
- month = nov # " 2-4",
- publisher = "SCITEPRESS",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, evolutionary computation, memory optimisation, multi-objective optimisation, autoregressive time series forecasting",
- isbn13 = "978-989-758-475-6",
- bibdate = "2020-11-23",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ijcci/ijcci2020.html#0002JCR20",
-
DOI = "
doi:10.5220/0010106501840191",
- abstract = "In Grammatical Evolution (GE) individuals occupy more space than required, that is, the Actual Length of the individuals is longer than their Effective Length. This has major implications for scaling GE to complex problems that demand larger populations and complex individuals. We show how these two lengths vary for different sizes of population, demonstrating that Effective Length is relatively independent of population size, but that the Actual Length is proportional to it. We introduce Grammatical Evolution Memory Optimisation (GEMO), a two-stage evolutionary system that uses a multi-objective approach to identify the optimal, or at least, near-optimal, genome length for the problem being examined. It uses a single run with a multi-objective fitness function defined to minimise the error for the problem being tackled along with maximising the ratio of Effective to Actual Genome Length leading to better use of memory and hence, computational speedup. Then, in Stage 2, stand",
-
notes = "IJCCI
Biocomputing Developmental Systems, University of Limerick, Irland",
