Created by W.Langdon from gp-bibliography.bib Revision:1.4951

- @InProceedings{Pawlak:2016:EuroGP,
- author = "Tomasz P. Pawlak",
- title = "Geometric Semantic Genetic Programming is Overkill",
- 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 = "246--260",
- organisation = "EvoStar",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-30668-1",
- DOI = "doi:10.1007/978-3-319-30668-1_16",
- abstract = "Recently, a new notion of Geometric Semantic Genetic Programming emerged in the field of automatic program induction from examples. Given that the induction problem is stated by means of function learning and a fitness function is a metric, GSGP uses geometry of solution space to search for the optimal program. We demonstrate that a program constructed by GSGP is indeed a linear combination of random parts. We also show that this type of program can be constructed in a predetermined time by much simpler algorithm and with guarantee of solving the induction problem optimally. We experimentally compare the proposed algorithm to GSGP on a set of symbolic regression, Boolean function synthesis and classifier induction problems. The proposed algorithm is superior to GSGP in terms of training-set fitness, size of produced programs and computational cost, and generalizes on test-set similarly to GSGP.",
- notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",
- }

Genetic Programming entries for Tomasz Pawlak