Study on Learning and Evolution of Genetic Network Programming and Its Application

遗传网络规划的学习进化及其应用研究

• 批准号: 17360186
• 负责人: HIRASAWA Kotaro
• 金额: $ 9.48万
• 依托单位: Waseda University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17360186/
• 关键词: Evolutionary computation; Genetic Algorithm; Genetic Programming; Data mining; Association rules; 進化型計算; 学習; 共生; 機能局在; マクロノード; 進化; ニューラルネットワーク

Genetic Algorithm (GA), a method which imitates nature and living things, was proposed as a model which explains the adaptive process of the system of nature. In addition, Genetic Programming (GP) was proposed to deal with knowledge expression, programs, concept trees and so on. Many algorithms of these kinds of evolutionary computations have been developed and applied to real world problems. However, these methods represent their solutions using strings or tree structures, so the abilities of representing solutions and the evolution are not enough in terms of the system modeling and the optimization. Therefore, Genetic Network Programming was proposed and its effectiveness was confirmed. In this research, the following extensions and the real world applications of GNP are studied.1. Extensions of GNPCombination of learning and evolution, function localized GNP, GNP with Macro nodes, GNP with Symbiotic learning and evolution, Variable-size GNP2. Applications of GNPElevator Group Supervisory Control Systems, Data Mining, Stock Trading Model In GNP, judgment nodes and processing nodes are connected with directed links with each other. The graph structure contributes to reusing nodes, good expression ability, simplicity of understanding the algorithms and good performance of evolution. In addition, unlike Finite Automata, GNP uses only the necessary information at the current time to judge the situation, so GNP can be evolved under Partially Observable Markov Decision Process. As a result, the applicable field of GNP can be extended.

遗传算法是一种模仿自然界和生物的方法，被提出来作为解释自然系统自适应过程的模型。此外，遗传程序设计（Genetic Programming，GP）被提出来处理知识表示、程序、概念树等问题，这类进化计算的许多算法已经被开发并应用于真实的世界问题。然而，这些方法使用字符串或树结构来表示它们的解，因此就系统建模和优化而言，表示解和演化的能力是不够的。因此，提出了遗传网络规划，并证实了其有效性。在本研究中，我们将探讨GNP的下列延伸及在真实的世界中的应用。GNP的扩展学习和进化的结合，功能本地化的GNP，GNP与宏节点，GNP与共生学习和进化，可变大小的GNP 2。GNPE电梯群监控系统、数据挖掘、股票交易模型的应用在GNPE电梯群监控系统中，判断节点和处理节点通过有向链路相互连接。图的结构有助于重用节点，良好的表达能力，简单的理解算法和良好的进化性能。此外，与有限自动机不同，GNP只使用当前时刻的必要信息来判断情况，因此GNP可以在部分可观测马尔可夫决策过程下进化。从而扩大了GNP的适用范围。

项目成果

Trading Rules on Stock Markets Using Genetic Network Programming with Candle Chart

利用蜡烛图遗传网络编程的股票市场交易规则

• 发表时间: 2007
• 期刊: T. SICE Vol. 43, No. 4
• 作者: S. Mabu;Y. Izumi;K Hirasawa;T. Furuzuki
• 通讯作者: T. Furuzuki

Basic Study of Elevator Group Supervisory Control System using Genetic Network Programming

基于遗传网络编程的电梯群监控系统基础研究

• 发表时间: 2005
• 期刊: IEEJ Trans EIS Vol. 125, No. 7
• 作者: T. Eguchi;J. Zhou;K. Hirasawa;T Furuzuki;M. Sandor
• 通讯作者: M. Sandor

Realizing Functional Localization Using Genetic Network Programming with Important Index

利用重要指标遗传网络规划实现功能定位

• 发表时间: 2006
• 期刊: Journal of Advanced Computational Intelligence and Intelligent Informatics 10
• 作者: K.Taboada;E.Gonzales;K.Shimada;S.Mabu;K.Hirasawa;J.Hu;S. Eto
• 通讯作者: S. Eto

プログラムサイズ可変型マクロノードつき遣伝的ネットワークプログラミング

具有可变程序大小宏节点的通用网络编程

• 发表时间: 2006
• 期刊: 電気学会論文誌C 126(4)
• 作者: 間普;畠山;中越;平澤;古月
• 通讯作者: 古月

重要度付きGenetic Network Programmingを用いた株式売買モデル

使用遗传网络编程的股票交易模型具有重要性

• 发表时间: 2006
• 期刊: 自動制御学会論文集 42(5)
• 作者: 泉;平澤;古月
• 通讯作者: 古月