The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform
Xavier Bonnin - One of the best experts on this subject based on the ideXlab platform.
-
Subroutines for some plasma surface interaction processes physical sputtering chemical erosion radiation enhanced sublimation backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented.
-
Subroutines for some plasma surface interaction processes: Physical sputtering, chemical erosion, radiation enhanced sublimation, backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented. © 2004 Elsevier B.V. All rights reserved.
Kotaro Hirasawa - One of the best experts on this subject based on the ideXlab platform.
-
Towards automatic discovery and reuse of Subroutines in Variable Size Genetic Network Programming
2012 IEEE Congress on Evolutionary Computation, 2012Co-Authors: Bing Li, Shingo Mabu, Xianneng Li, Kotaro HirasawaAbstract:This paper presents an algorithm to discover and reuse Subroutines in Variable Size Genetic Network Programming (GNPvs) called Subroutine embedded GNPvs (SGNPvs). GNPvs is a general type of GNP, which has a direct graph representation with changeable size. In order to improve the performance of GNPvs, SGNPvs has been proposed, in which a subroutine mechanism has been introduced to GNPvs by module acquisition. In SGNPvs, useful subgraphs are extracted and reused for individuals. Through extracting new Subroutines to replace the old Subroutines, SGNPvs can evolve the Subroutines as well as evolve the individuals. The simulation results verify the performance of SGNPvs on a well-known dynamic multi-agent test bed - Tileworld.
-
genetic network programming with Subroutines for automatic program generation
Ieej Transactions on Electrical and Electronic Engineering, 2012Co-Authors: Bing Li, Shingo Mabu, Kotaro HirasawaAbstract:In this paper, a subroutine mechanism is introduced in genetic network programming for automatic program generation (GNP-APG). The proposed method named GNP with Subroutines for APG (GNPsr-APG) is an extension of the algorithm of GNP-APG, where hierarchy programs are generated: in other words, programs that contain a main function and Subroutines are generated. The proposed method automatically defines the main function and use of the potentially useful Subroutines during evolution. By using Subroutines, a complex program can be decomposed to several simple programs which are obtained more easily. Moreover, these Subroutines are called many times from a main program, which results in reducing the size of the program significantly. The simulation results verify that the proposed method can improve the performance of GNP-APG and reduce the size of the program. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
-
Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural Subroutines
SICE Annual Conference 2011, 2011Co-Authors: Yunqing Gu, Shingo Mabu, Yang Yang, Jianhua Li, Kotaro HirasawaAbstract:In this paper, Genetic Network Programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural Subroutines. Subroutine node - a new kind of node which works like ADF (Automatically Defined Function) in Genetic Programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural Subroutines with different structures are used to improve the performance of GNP-Sarsa with Subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural Subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.
-
Multi-Subroutines in Genetic Network Programming-Sarsa for trading rules on stock markets
SICE Annual Conference 2011, 2011Co-Authors: Yang Yang, Yunqing Gu, Shingo Mabu, Kotaro HirasawaAbstract:This paper describes a decision-making model for creating trading rules on stock markets using a graph-based evolutionary algorithm named Genetic Network Programming-Sarsa (GNP-Sarsa) and multi-Subroutines. The method is developed for discovering the repetitive subgraphs over the entire graph structure and modularizing them as Subroutines, which results in substantially fastening the search by suppressing redundant search and results in reducing the overfitting leading to the improvement of the generalization capability. The following two are discussed: 1) varying the number of subroutine nodes in the main program and 2) varying the kind of Subroutines to be generated. The experimental results on the stock markets show that the proposed method can generate more efficient and robust trading models and obtain much higher profits.
-
GNP-Sarsa with Subroutines for trading rules on stock markets
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Yang Yang, Shingo Mabu, Jianhua Li, Kotaro HirasawaAbstract:The purpose of this paper is to study how to improve the evolution of GNP-Sarsa with Subroutines and its application to trading rules on stock markets. Recently, a successful study, namely GNP-Sarsa, shows us its effectiveness and powerfulness, which combines sophisticated diversified search ability for structures using evolution and intensified search ability of RL for many technical indices and candlestick charts. But, another advantage of GNP, the compact structure becomes weak by GNP-Sarsa, and then the choice of trade-off between efficiency and compactness is difficult. So, we intend to solve this problem by extending the basic structure of GNP. A new method named GNP with Subroutines has been proposed, which adds new kind of nodes named subroutine nodes to main GNP to call a corresponding subprogram (subroutine). This reusable subroutine works like small scale GNP and has also judgment nodes and processing nodes like GNP. This paper introduces the concurrent evolution of GNP and subroutine. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness. The results show that the proposed approach can provide reasonable opportunities for complex solutions to evolve.
Manya Warrier - One of the best experts on this subject based on the ideXlab platform.
-
Subroutines for some plasma surface interaction processes physical sputtering chemical erosion radiation enhanced sublimation backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented.
-
Subroutines for some plasma surface interaction processes: Physical sputtering, chemical erosion, radiation enhanced sublimation, backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented. © 2004 Elsevier B.V. All rights reserved.
Ralf Schneider - One of the best experts on this subject based on the ideXlab platform.
-
Subroutines for some plasma surface interaction processes physical sputtering chemical erosion radiation enhanced sublimation backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented.
-
Subroutines for some plasma surface interaction processes: Physical sputtering, chemical erosion, radiation enhanced sublimation, backscattering and thermal evaporation
Computer Physics Communications, 2004Co-Authors: Manya Warrier, Ralf Schneider, Xavier BonninAbstract:A suite of FORTRAN Subroutines/functions to generate data using empirical formulas for physical sputtering of mono-atomic targets for any elemental incident ion (atom), chemical erosion of graphite, Radiation Enhanced Sublimation (RES) of graphite, the number and energy backscattering coefficients for any elemental incident ion (atom) on a compound target and thermal evaporation of graphite is presented. Since chemical erosion, RES and thermal evaporation depend on the surface temperature of graphite, a subroutine implementing the 1-D heat diffusion equation to determine the temperature of any plasma-facing graphite surface is implemented. As an example to illustrate the use of these Subroutines/functions, a simple model for the erosion of a plasma-facing surface, consisting of a simple collisionless sheath model, a 1-dimensional steady state heat diffusion model and 0-dimensional steady state particle balance at the target is developed and a sample listing of the program is presented. © 2004 Elsevier B.V. All rights reserved.
Shingo Mabu - One of the best experts on this subject based on the ideXlab platform.
-
Towards automatic discovery and reuse of Subroutines in Variable Size Genetic Network Programming
2012 IEEE Congress on Evolutionary Computation, 2012Co-Authors: Bing Li, Shingo Mabu, Xianneng Li, Kotaro HirasawaAbstract:This paper presents an algorithm to discover and reuse Subroutines in Variable Size Genetic Network Programming (GNPvs) called Subroutine embedded GNPvs (SGNPvs). GNPvs is a general type of GNP, which has a direct graph representation with changeable size. In order to improve the performance of GNPvs, SGNPvs has been proposed, in which a subroutine mechanism has been introduced to GNPvs by module acquisition. In SGNPvs, useful subgraphs are extracted and reused for individuals. Through extracting new Subroutines to replace the old Subroutines, SGNPvs can evolve the Subroutines as well as evolve the individuals. The simulation results verify the performance of SGNPvs on a well-known dynamic multi-agent test bed - Tileworld.
-
genetic network programming with Subroutines for automatic program generation
Ieej Transactions on Electrical and Electronic Engineering, 2012Co-Authors: Bing Li, Shingo Mabu, Kotaro HirasawaAbstract:In this paper, a subroutine mechanism is introduced in genetic network programming for automatic program generation (GNP-APG). The proposed method named GNP with Subroutines for APG (GNPsr-APG) is an extension of the algorithm of GNP-APG, where hierarchy programs are generated: in other words, programs that contain a main function and Subroutines are generated. The proposed method automatically defines the main function and use of the potentially useful Subroutines during evolution. By using Subroutines, a complex program can be decomposed to several simple programs which are obtained more easily. Moreover, these Subroutines are called many times from a main program, which results in reducing the size of the program significantly. The simulation results verify that the proposed method can improve the performance of GNP-APG and reduce the size of the program. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
-
Trading rules on stock markets using Genetic Network Programming-Sarsa Learning with plural Subroutines
SICE Annual Conference 2011, 2011Co-Authors: Yunqing Gu, Shingo Mabu, Yang Yang, Jianhua Li, Kotaro HirasawaAbstract:In this paper, Genetic Network Programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural Subroutines. Subroutine node - a new kind of node which works like ADF (Automatically Defined Function) in Genetic Programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural Subroutines with different structures are used to improve the performance of GNP-Sarsa with Subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural Subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.
-
Multi-Subroutines in Genetic Network Programming-Sarsa for trading rules on stock markets
SICE Annual Conference 2011, 2011Co-Authors: Yang Yang, Yunqing Gu, Shingo Mabu, Kotaro HirasawaAbstract:This paper describes a decision-making model for creating trading rules on stock markets using a graph-based evolutionary algorithm named Genetic Network Programming-Sarsa (GNP-Sarsa) and multi-Subroutines. The method is developed for discovering the repetitive subgraphs over the entire graph structure and modularizing them as Subroutines, which results in substantially fastening the search by suppressing redundant search and results in reducing the overfitting leading to the improvement of the generalization capability. The following two are discussed: 1) varying the number of subroutine nodes in the main program and 2) varying the kind of Subroutines to be generated. The experimental results on the stock markets show that the proposed method can generate more efficient and robust trading models and obtain much higher profits.
-
GNP-Sarsa with Subroutines for trading rules on stock markets
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Yang Yang, Shingo Mabu, Jianhua Li, Kotaro HirasawaAbstract:The purpose of this paper is to study how to improve the evolution of GNP-Sarsa with Subroutines and its application to trading rules on stock markets. Recently, a successful study, namely GNP-Sarsa, shows us its effectiveness and powerfulness, which combines sophisticated diversified search ability for structures using evolution and intensified search ability of RL for many technical indices and candlestick charts. But, another advantage of GNP, the compact structure becomes weak by GNP-Sarsa, and then the choice of trade-off between efficiency and compactness is difficult. So, we intend to solve this problem by extending the basic structure of GNP. A new method named GNP with Subroutines has been proposed, which adds new kind of nodes named subroutine nodes to main GNP to call a corresponding subprogram (subroutine). This reusable subroutine works like small scale GNP and has also judgment nodes and processing nodes like GNP. This paper introduces the concurrent evolution of GNP and subroutine. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness. The results show that the proposed approach can provide reasonable opportunities for complex solutions to evolve.
