Guidelines for defining benchmark problems in Genetic Programming
|Title:||Guidelines for defining benchmark problems in Genetic Programming||Authors:||Nicolau, Miguel
|Permanent link:||http://hdl.handle.net/10197/8249||Date:||28-May-2015||Online since:||2017-01-04T12:52:33Z||Abstract:||The field of Genetic Programming has recently seen a surge of attention to the fact that benchmarking and comparison of approaches is often done in non-standard ways, using poorly designed comparison problems. We raise some issues concerning the design of benchmarks, within the domain of symbolic regression, through experimental evidence. A set of guidelines is provided, aiming towards careful definition and use of artificial functions as symbolic regression benchmarks.||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2015 IEEE||Keywords:||Genetic programming; Benchmarks; Symbolic regression; Regression; Model building||DOI:||10.1109/CEC.2015.7257019||Language:||en||Status of Item:||Peer reviewed||Conference Details:||IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, Proceedings, Sendai, Japan, May, 2015|
|Appears in Collections:||Business Research Collection|
CASL Research Collection
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.