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

大学院情報生産システム研究科

研究成果: Paper › 査読

抄録

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.

本文言語	English
ページ	605-610
ページ数	6
出版ステータス	Published - 2004 12 1
イベント	SICE Annual Conference 2004 - Sapporo, Japan 継続期間: 2004 8 4 → 2004 8 6

Conference

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

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

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引用スタイル

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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.",

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AU - Mabu, Shingo

AU - Hirasawa, Kotaro

AU - Hu, Jinglu

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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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