- Improved distance-based evaluation measures (E-NE and A-NE) as explained on the homepage.
- Support for more data types (various marker formats, phenotypic traits, precomputed distance matrix).
- Reimplemented from scratch using the JAMES framework.
- Default optimization algorithm is again the parallel tempering algorithm (a.k.a. REMC) as in Core Hunter 1.
The Mixed Replica search from Core Hunter 2 is no longer used.
We switched back to parallel tempering since the more complex Mixed Replica search used by Core Hunter 2 is no longer
needed due to the improved distance-based evaluation measures, that are more easily optimized with local searches as
compared to minimum distance (which was one of the objectives in Core Hunter 2). Core Hunter 3 also provides a fast
mode in which a basic stochastic hill-climber is used (referred to as plain local search in the Core Hunter 2 paper, and
as random descent in JAMES and Core Hunter 3).
You can find some more documentation in the R package. For example, have a look at
> ?sampleCore
A new paper with more details is on its way. For now, please cite the original papers if you use Core Hunter 3 for your research.
If you have any more questions, let us know.
Best regards,
Herman
Op donderdag 2 maart 2017 09:17:00 UTC+1 schreef Pietro Delfino: