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Traveling Time Prediction using Isolation Rules
Koaru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A method for traveling time prediction is proposed using Genetic Network Programming(GNP) based data mining. The method extracts the rules named Isolation Rules, that is, a kind of association rules having the consequent part with the narrow distribution of continuous values. A set of isolation rules is applied to continuous value prediction. The database of the traveling time of the cocused route with traffic information is generated and isolation rules on the traveling time of the route are extracted. Traveling time prediction is done considering the matching rate of the isolation rules with the current traffic conditions.
Global Portfolio Diversification by Genetic Relation Algorithm
Victor Parque,Shingo Mabu,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Capital flows are increasingly intertwined globally and, consequently, have brought advantages to global investment strategies. Having a global view of portfolio allocation brings about the diversification of risks in investments. In this paper, a framework to select and optimize as set portfolios in relevant financial markets for short term investment is proposed. In this approach, beta portfolio is a measure of intert wined as set risks and Genetic Relation Algorithm is the evolutionary computing framework for building comprehesible and compact structures of global as sets. The algorithm evaluates there lational beta coefficient among as sets and generates a robust portfolio in the last generation. Simulations are doneusing stocks, bond sand currencies as three major as set classes,i.e., the data corresponding to relevant financial markets in USA, Europe and Asia, and the efficiency of the proposed method is compared with traditional Capital As set Pricing Model(CAPM) for building portfolios.
Genetic Network Programming with General Individual Reconstruction
Fengming Ye,Shingo Mabu,Lutao Wang,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Genetic Network Programming(GNP) which has been developed for dealing with problems in dynamic envi-ronments is a newly propose devolutionary approach with the data structure of directed graphs. GNP has been used in many different areas such as datamining, extracting trading rules of stock markets, elevator supervised control systems, etc and has obtained some out standing results. Focusing on GNP’s distinguishing expressionability of the graph structure, this paper proposes a method named Genetic Network Program-ming with General Individual Recon struction(GNP with GIR) which reconstructs the gene of randomly selected individuals and then under goes the special genetic operations by using the transition information of betterin dividuals. The unique indi-vidual reconstruction and genetic operations make individuals not only learn the experiences of better individuals but also strength enexploratio and exploration ability. GNP with GIR will be applied to the tile-world which is an excellent bench mark for evaluating the proposed architecture. The performances of GNP with GIR will becompared with conventional GNP demonstrating its superiority.