Semantic Geometric Initialization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Pawlak2:2016:EuroGP,

• author = "Tomasz P. Pawlak and Krzysztof Krawiec",
• title = "Semantic Geometric Initialization",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "261--277",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_17",
• abstract = "A common approach in Geometric Semantic Genetic Programming (GSGP) is to seed initial populations using conventional, semantic-unaware methods like Ramped Half-and-Half. We formally demonstrate that this may limit GSGP's ability to find a program with the sought semantics. To overcome this issue, we determine the desired properties of geometric-aware semantic initialization and implement them in Semantic Geometric Initialization (\textsc{Sgi}) algorithm, which we instantiate for symbolic regression and Boolean function synthesis problems. Properties of \textsc{Sgi} and its impact on GSGP search are verified experimentally on nine symbolic regression and nine Boolean function synthesis benchmarks. When assessed experimentally, \textsc{Sgi} leads to superior performance of GSGP search: better best-of-run fitness and higher probability of finding the optimal program.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Tomasz Pawlak Krzysztof Krawiec

Citations