Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution

Bing Li; Xianneng Li; Shingo Mabu; Kotaro Hirasawa

doi:10.1002/tee.21865

Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution

Bing Li, Xianneng Li, Shingo Mabu, Kotaro Hirasawa

Information, Production and Systems Research Center

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.

Original language	English
Pages (from-to)	348-356
Number of pages	9
Journal	IEEJ Transactions on Electrical and Electronic Engineering
Volume	8
Issue number	4
DOIs	https://doi.org/10.1002/tee.21865
Publication status	Published - 2013 Jul

Keywords

Binomial distribution
Crossover
Genetic network programming
Tileworld
Variable size

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1002/tee.21865

Fingerprint Dive into the research topics of 'Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution'. Together they form a unique fingerprint.

Cite this

@article{302cb400db5344e6bb603328f6c2da26,

title = "Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution",

abstract = "Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.",

keywords = "Binomial distribution, Crossover, Genetic network programming, Tileworld, Variable size",

author = "Bing Li and Xianneng Li and Shingo Mabu and Kotaro Hirasawa",

year = "2013",

month = jul,

doi = "10.1002/tee.21865",

language = "English",

volume = "8",

pages = "348--356",

journal = "IEEJ Transactions on Electrical and Electronic Engineering",

issn = "1931-4973",

publisher = "John Wiley and Sons Inc.",

number = "4",

}

TY - JOUR

T1 - Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution

AU - Li, Bing

AU - Li, Xianneng

AU - Mabu, Shingo

AU - Hirasawa, Kotaro

PY - 2013/7

Y1 - 2013/7

N2 - Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.

AB - Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.

KW - Binomial distribution

KW - Crossover

KW - Genetic network programming

KW - Tileworld

KW - Variable size

UR - http://www.scopus.com/inward/record.url?scp=84879251977&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879251977&partnerID=8YFLogxK

U2 - 10.1002/tee.21865

DO - 10.1002/tee.21865

M3 - Article

AN - SCOPUS:84879251977

VL - 8

SP - 348

EP - 356

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

SN - 1931-4973

IS - 4

ER -