Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

Man Leung WONG; Kwong Sak LEUNG

doi:10.5555/1595536.1595566

Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.

Original language	English
Title of host publication	Proceedings of the 1st annual conference on genetic programming
Publisher	MIT Press
Pages	238-246
ISBN (Print)	9780262611275
DOIs	https://doi.org/10.5555/1595536.1595566
Publication status	Published - Jul 1996
Externally published	Yes
Event	1st annual conference on genetic programming - Stanford University, California, United States Duration: 28 Jul 1996 → 31 Jul 1996 https://dl.acm.org/doi/proceedings/10.5555/1595536

Conference

Conference	1st annual conference on genetic programming
Country/Territory	United States
City	California
Period	28/07/96 → 31/07/96
Internet address	https://dl.acm.org/doi/proceedings/10.5555/1595536

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title = "Learning Recursive Functions from Noisy Examples using Generic Genetic Programming",

abstract = "One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.",

author = "WONG, {Man Leung} and LEUNG, {Kwong Sak}",

year = "1996",

month = jul,

doi = "10.5555/1595536.1595566",

language = "English",

isbn = "9780262611275",

pages = "238--246",

booktitle = "Proceedings of the 1st annual conference on genetic programming",

publisher = "MIT Press",

note = "1st annual conference on genetic programming ; Conference date: 28-07-1996 Through 31-07-1996",

url = "https://dl.acm.org/doi/proceedings/10.5555/1595536",

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AU - WONG, Man Leung

AU - LEUNG, Kwong Sak

PY - 1996/7

Y1 - 1996/7

N2 - One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.

AB - One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.

U2 - 10.5555/1595536.1595566

DO - 10.5555/1595536.1595566

M3 - Conference paper (refereed)

SN - 9780262611275

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EP - 246

BT - Proceedings of the 1st annual conference on genetic programming

PB - MIT Press

T2 - 1st annual conference on genetic programming

Y2 - 28 July 1996 through 31 July 1996

ER -

Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

Abstract

Conference

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