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 | |
| Publication status | Published - 2013 Jul |
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Keywords
- Binomial distribution
- Crossover
- Genetic network programming
- Tileworld
- Variable size
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution. / Li, Bing; Li, Xianneng; Mabu, Shingo; Hirasawa, Kotaro.
In: IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 4, 07.2013, p. 348-356.Research output: Contribution to journal › Article
}
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 -