A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning

Xianneng Li, Bing Li, Shingo Mabu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.

Original languageEnglish
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Pages37-44
Number of pages8
DOIs
Publication statusPublished - 2011
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA
Duration: 2011 Jun 52011 Jun 8

Other

Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
CityNew Orleans, LA
Period11/6/511/6/8

Fingerprint

Graph Algorithms
Reinforcement learning
Chromosomes
Reinforcement Learning
Chromosome
Probabilistic Model
Autonomous Robots
Robot Control
Directed graphs
Directed Graph
Building Blocks
Pairwise
Robots
Experimental Results
Vertex of a graph
Interaction

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Li, X., Li, B., Mabu, S., & Hirasawa, K. (2011). A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (pp. 37-44). [5949595] https://doi.org/10.1109/CEC.2011.5949595

A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. / Li, Xianneng; Li, Bing; Mabu, Shingo; Hirasawa, Kotaro.

2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. p. 37-44 5949595.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, X, Li, B, Mabu, S & Hirasawa, K 2011, A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. in 2011 IEEE Congress of Evolutionary Computation, CEC 2011., 5949595, pp. 37-44, 2011 IEEE Congress of Evolutionary Computation, CEC 2011, New Orleans, LA, 11/6/5. https://doi.org/10.1109/CEC.2011.5949595
Li X, Li B, Mabu S, Hirasawa K. A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. p. 37-44. 5949595 https://doi.org/10.1109/CEC.2011.5949595
Li, Xianneng ; Li, Bing ; Mabu, Shingo ; Hirasawa, Kotaro. / A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning. 2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. pp. 37-44
@inproceedings{0753cdbd80984c4e84ad83e371d3769d,
title = "A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning",
abstract = "This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.",
author = "Xianneng Li and Bing Li and Shingo Mabu and Kotaro Hirasawa",
year = "2011",
doi = "10.1109/CEC.2011.5949595",
language = "English",
isbn = "9781424478347",
pages = "37--44",
booktitle = "2011 IEEE Congress of Evolutionary Computation, CEC 2011",

}

TY - GEN

T1 - A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning

AU - Li, Xianneng

AU - Li, Bing

AU - Mabu, Shingo

AU - Hirasawa, Kotaro

PY - 2011

Y1 - 2011

N2 - This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.

AB - This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.

UR - http://www.scopus.com/inward/record.url?scp=80051965781&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051965781&partnerID=8YFLogxK

U2 - 10.1109/CEC.2011.5949595

DO - 10.1109/CEC.2011.5949595

M3 - Conference contribution

SN - 9781424478347

SP - 37

EP - 44

BT - 2011 IEEE Congress of Evolutionary Computation, CEC 2011

ER -