Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems

Shingo Mabu; Kotaro Hirasawa; Jinglu Hu

Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems

Shingo Mabu^*, Kotaro Hirasawa, Jinglu Hu

^*Corresponding author for this work

Graduate School of Information, Production and Systems

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

Abstract

A new graph-based evolutionary algorithm named "Genetic Network Programming, GNP" has been proposed. GNP represents its solutions as graph structures which have distinguished expression ability. In this paper, we propose GNP with Reinforcement Learning. Evolutionary algorithm of GNP makes a very compact graph structure and Reinforcement Learning of GNP improves search speed for solutions.

Original language	English
Pages	605-610
Number of pages	6
Publication status	Published - 2004 Dec 1
Event	SICE Annual Conference 2004 - Sapporo, Japan Duration: 2004 Aug 4 → 2004 Aug 6

Conference

Conference	SICE Annual Conference 2004
Country/Territory	Japan
City	Sapporo
Period	04/8/4 → 04/8/6

Keywords

Agent
Genetic Programming
Graph structure
Reinforcement Learning

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

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title = "Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems",

abstract = "A new graph-based evolutionary algorithm named {"}Genetic Network Programming, GNP{"} has been proposed. GNP represents its solutions as graph structures which have distinguished expression ability. In this paper, we propose GNP with Reinforcement Learning. Evolutionary algorithm of GNP makes a very compact graph structure and Reinforcement Learning of GNP improves search speed for solutions.",

keywords = "Agent, Genetic Programming, Graph structure, Reinforcement Learning",

author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",

year = "2004",

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note = "SICE Annual Conference 2004 ; Conference date: 04-08-2004 Through 06-08-2004",

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T1 - Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems

AU - Mabu, Shingo

AU - Hirasawa, Kotaro

AU - Hu, Jinglu

PY - 2004/12/1

Y1 - 2004/12/1

N2 - A new graph-based evolutionary algorithm named "Genetic Network Programming, GNP" has been proposed. GNP represents its solutions as graph structures which have distinguished expression ability. In this paper, we propose GNP with Reinforcement Learning. Evolutionary algorithm of GNP makes a very compact graph structure and Reinforcement Learning of GNP improves search speed for solutions.

AB - A new graph-based evolutionary algorithm named "Genetic Network Programming, GNP" has been proposed. GNP represents its solutions as graph structures which have distinguished expression ability. In this paper, we propose GNP with Reinforcement Learning. Evolutionary algorithm of GNP makes a very compact graph structure and Reinforcement Learning of GNP improves search speed for solutions.

KW - Agent

KW - Genetic Programming

KW - Graph structure

KW - Reinforcement Learning

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M3 - Paper

AN - SCOPUS:12744274815

SP - 605

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T2 - SICE Annual Conference 2004

Y2 - 4 August 2004 through 6 August 2004

ER -

Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems

Abstract

Conference

Keywords

ASJC Scopus subject areas

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Cite this