Share and Export
Share and Export
Galvan-Lopez, Edgar, McDermott, James, O'Neill, Michael and Brabazon, Anthony (2010) Defining locality in genetic programming to predict performance. In: Evolutionary Computation (CEC), 2010 IEEE Congress on, August 2010, Barcelona, Spain.
Preview
EG_defining.pdf
Download (206kB) | Preview
Abstract
A key indicator of problem difficulty in evolutionary computation problems is the landscape's locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype-fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15422 |
| Identification Number: | 10.1109/CEC.2010.5586095 |
| Depositing User: | Edgar Galvan |
| Date Deposited: | 07 Feb 2022 14:46 |
| Refereed: | Yes |
| URI: | https://mu.eprints-hosting.org/id/eprint/15422 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
- Item control page
Downloads
Downloads per month over past year
