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RISS 인기검색어
- 검색키워드
- 저자 : Fengming Ye (검색결과 2 건)
- 무료
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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.
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Rule Accumulation Method with Modified Fitness Function based on Genetic Network Programming
Lutao Wang,Shingo Mabu,Fengming Ye,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Genetic Network Programming (GNP) extended from GA and GP is competent for the complex problems in dynamic environments because of its directed graph structure, reusability of nodes and implicit memory function. In this paper, a new method to extract and accumulate rules from GNP is proposed. The general idea is to update the fitness values of the rules accumulatively, rather than just replacing them in the former research. That is, the rules which appear frequently in different generations are given higher fitness values because they represent good universal experiences from the past behaviors. By extracting the rules during the evolutionary period and then matching them with agents’ environments, we could guide the agents properly and get better rewards. In order to test the efficiency and effectiveness of the proposed method, we applied the proposed method to the problem of Tile-world as the simulation environment. Simulation results demonstrate the effectiveness of the proposed method.
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