Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Gathercole:1997:lef,

• author = "Chris Gathercole and Peter Ross",
• title = "Tackling the {Boolean} Even N Parity Problem with Genetic Programming and Limited-Error Fitness",
• booktitle = "Genetic Programming 1997: Proceedings of the Second Annual Conference",
• editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max Garzon and Hitoshi Iba and Rick L. Riolo",
• year = "1997",
• month = "13-16 " # jul,
• keywords = "genetic algorithms, genetic programming",
• pages = "119--127",
• address = "Stanford University, CA, USA",
• publisher_address = "San Francisco, CA, USA",
• publisher = "Morgan Kaufmann",
• broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_for_public_gp97_lef.ps.gz",
• URL = "http://citeseer.ist.psu.edu/79389.html",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.1298",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.26.1298.pdf",
• size = "9 pages",
• abstract = "This paper presents Limited Error Fitness (LEF), a modification to the standard supervised learning approach in Genetic Programming (GP), in which an individual's fitness score is based on how many cases remain uncovered in the ordered training set after the individual exceeds an error limit. The training set order and the error limit are both altered dynamically in response to the performance of the fittest individual in the previous generation. LEF allows standard GP to readily solve the Boolean Even N Parity problem (a very hard classification problem for GP) for N=6 and N=7 with a population size of 400, otherwise, Automatically Defined Functions, a more powerful representation, and much larger populations, are required for GP to solve for N>5. Individual fitness evaluations run more quickly, but LEF usually requires many more generations. Also a smaller population size allows GP to be run on smaller computers at a reasonable speed. LEF changes the dynamics of GP, preventing premature convergence and allows a hard problem to be presented, in effect, as a series of subproblems",
• notes = "GP-97 slides at http://www.dai.ed.ac.uk/students/chrisg/gp97/lef/slides.html",

}

Genetic Programming entries for Chris Gathercole Peter Ross

Citations