Stack-Based Genetic Programming

author = "Tim Perkis",

title = "Stack-Based Genetic Programming",

year = "1994",

booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence",

publisher = "IEEE Press",

volume = "1",

pages = "148--153",

address = "Orlando, Florida, USA",

month = "27-29 " # jun,

keywords = "genetic algorithms, genetic programming, optimum representations, stack-based genetic programming, stack-based virtual machine, learning (artificial intelligence), AI, optimisation",

ISBN = "0-7803-1899-4",

URL = "

https://www.perkis.com/_site/writings/trp_stackGP.pdf",

URL = "

http://citeseer.ist.psu.edu/432690.html",

DOI = "

doi:10.1109/ICEC.1994.350025",

size = "6 pages",

abstract = "Some recent work in the field of genetic programming (GP) has been concerned with finding optimum representations for evolvable and efficient computer programs. This paper describes a new GP system in which target programs run on a stack-based virtual machine. The system is shown to have certain advantages in terms of efficiency and simplicity of implementation, and for certain problems, its effectiveness is shown to be comparable or superior to current methods.",

notes = "Report 93-11-A Antelope Engineering, Albany, California, See also http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/stack-gp.ps.gz",