Self-adaptive routing method based on learning classifier systems

Chungyuan Huang; Chuentsai Sun; Chenghsien Yu; Chaofang Chen

Self-adaptive routing method based on learning classifier systems

Chungyuan Huang^*, Chuentsai Sun, Chenghsien Yu, Chaofang Chen

^*Corresponding author for this work

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

Successful computer and internet networks require carefully designed routing protocols. The authors report on their attempt to apply evolutionary computations-that is, to place a learning classifier system on individual routers - to solve routing problems. We found that learning classifier systems are capable of fulfilling traditional routing protocol tasks (e.g., establishing routing tables) after a short period of training. Furthermore, they are capable of adapting to changing network environments and choosing the most efficient path available. Results from our experiments show that the system outperforms shortest path algorithms.

Original language	English
Pages	417-421
Number of pages	5
State	Published - 2004
Externally published	Yes
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 06 2004 → 19 06 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Genetic algorithms
Learning classifier systems
Reinforcement learning
Routing protocol
Self-adaptive

Cite this

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abstract = "Successful computer and internet networks require carefully designed routing protocols. The authors report on their attempt to apply evolutionary computations-that is, to place a learning classifier system on individual routers - to solve routing problems. We found that learning classifier systems are capable of fulfilling traditional routing protocol tasks (e.g., establishing routing tables) after a short period of training. Furthermore, they are capable of adapting to changing network environments and choosing the most efficient path available. Results from our experiments show that the system outperforms shortest path algorithms.",

keywords = "Genetic algorithms, Learning classifier systems, Reinforcement learning, Routing protocol, Self-adaptive",

author = "Chungyuan Huang and Chuentsai Sun and Chenghsien Yu and Chaofang Chen",

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T1 - Self-adaptive routing method based on learning classifier systems

AU - Huang, Chungyuan

AU - Sun, Chuentsai

AU - Yu, Chenghsien

AU - Chen, Chaofang

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N2 - Successful computer and internet networks require carefully designed routing protocols. The authors report on their attempt to apply evolutionary computations-that is, to place a learning classifier system on individual routers - to solve routing problems. We found that learning classifier systems are capable of fulfilling traditional routing protocol tasks (e.g., establishing routing tables) after a short period of training. Furthermore, they are capable of adapting to changing network environments and choosing the most efficient path available. Results from our experiments show that the system outperforms shortest path algorithms.

AB - Successful computer and internet networks require carefully designed routing protocols. The authors report on their attempt to apply evolutionary computations-that is, to place a learning classifier system on individual routers - to solve routing problems. We found that learning classifier systems are capable of fulfilling traditional routing protocol tasks (e.g., establishing routing tables) after a short period of training. Furthermore, they are capable of adapting to changing network environments and choosing the most efficient path available. Results from our experiments show that the system outperforms shortest path algorithms.

KW - Genetic algorithms

KW - Learning classifier systems

KW - Reinforcement learning

KW - Routing protocol

KW - Self-adaptive

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M3 - 论文

AN - SCOPUS:4444238045

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T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

Self-adaptive routing method based on learning classifier systems

Abstract

Conference

Keywords

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