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Genetic Programming (genetic + programming)
Selected AbstractsGENETIC PROGRAMMING AND ITS APPLICATION IN REAL-TIME RUNOFF FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2001Soon Thiam Khu ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly. [source] Detecting New Forms of Network Intrusion Using Genetic ProgrammingCOMPUTATIONAL INTELLIGENCE, Issue 3 2004Wei Lu How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition operators, are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that a rule generated by GP has a low false positive rate (FPR), a low false negative rate and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate with low FPR. An alternative to the DARPA evaluation approach is also investigated. [source] Virus-evolutionary linear genetic programmingELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 1 2008Kenji Tamura Abstract Many kinds of evolutionary methods have been proposed. GA and GP in particular have demonstrated their effectiveness in various problems recently, and many systems have been proposed. One is Virus-Evolutionary Genetic Algorithm (VE-GA), and the other is Linear Genetic Programming in C (LGPC). The performance of each system has been reported. VE-GA is the coevolution system of host individuals and virus individuals. That can spread schema effectively among the host individuals by using virus infection and virus incorporation. LGPC implements the GP by representing the individuals to one dimension as if GA. LGPC can reduce a search cost of pointer and save machine memory, and can reduce the time to implement GP programs. We have proposed that a system introduce virus individuals in LGPC, and analyzed the performance of the system on two problems. Our system can spread schema among the population, and search solution effectively. The results of computer simulation show that this system can search for solution depending on LGPC applying problem's character compared with LGPC. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 32, 39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10030 [source] Time Series Modeling of Two- and Three-Phase Flow Boiling Systems with Genetic ProgrammingCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 11 2007M.-Y. Liu Abstract The time series of the physical parameters in boiling evaporators with vapor-liquid (V-L) two-phase and vapor-liquid-solid (V-L-S) three-phase external natural circulating flows exhibit nonlinear features. Hence, proper system evolution models may be built from the point of view of nonlinear dynamics. In this work, genetic programming (GP) was utilized to find the nonlinear modeling functions necessary to develop global explicit two-variable iteration models, using wall temperature signals measured from the heated tube in ordinary two-phase and three-phase fluidized bed evaporators. The model predictions agree well with the experimental data of the time series, which means that the models established with GP can adequately describe the dynamic evolution behavior of multi-phase flow boiling systems. [source] An elaborate education of basic genetic programming using C++COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 3 2010Nirod C. Sahoo Abstract Evolutionary search is a global search method based on natural selection. In engineering curriculum, these techniques are taught in courses like Evolutionary Computation, Engineering Optimization, etc. Genetic algorithm (GA) is popular among these algorithms. Genetic programming (GP), developed by John Koza, is a powerful extension of GA where a chromosome/computer program (CP) is coded as a rooted point-labeled tree with ordered branches. The search space is the space of all possible CPs (trees) consisting of functions and terminals appropriate to the problem domain. GP uses, like GA, crossover and mutation for evolution. Due to tree-structured coding of individuals, the initial population generation, genetic operators' use, and tree decoding for fitness evaluations demand careful computer programming. This article describes the programming steps of GP implementation (using C++ language) for students' easy understanding with pseudocodes for each step. Two application examples are also illustrated. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 434,448, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20165 [source] Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computationFRESHWATER BIOLOGY, Issue 10 2007DONG-KYUN KIM Summary 1. An ecological model was developed using genetic programming (GP) to predict the time-series dynamics of the diatom, Stephanodiscus hantzschii for the lower Nakdong River, South Korea. Eight years of weekly data showed the river to be hypertrophic (chl. a, 45.1 ± 4.19 ,g L,1, mean ± SE, n = 427), and S. hantzschii annually formed blooms during the winter to spring flow period (late November to March). 2. A simple non-linear equation was created to produce a 3-day sequential forecast of the species biovolume, by means of time series optimization genetic programming (TSOGP). Training data were used in conjunction with a GP algorithm utilizing 7 years of limnological variables (1995,2001). The model was validated by comparing its output with measurements for a specific year with severe blooms (1994). The model accurately predicted timing of the blooms although it slightly underestimated biovolume (training r2 = 0.70, test r2 = 0.78). The model consisted of the following variables: dam discharge and storage, water temperature, Secchi transparency, dissolved oxygen (DO), pH, evaporation and silica concentration. 3. The application of a five-way cross-validation test suggested that GP was capable of developing models whose input variables were similar, although the data are randomly used for training. The similarity of input variable selection was approximately 51% between the best model and the top 20 candidate models out of 150 in total (based on both Root Mean Squared Error and the determination coefficients for the test data). 4. Genetic programming was able to determine the ecological importance of different environmental variables affecting the diatoms. A series of sensitivity analyses showed that water temperature was the most sensitive parameter. In addition, the optimal equation was sensitive to DO, Secchi transparency, dam discharge and silica concentration. The analyses thus identified likely causes of the proliferation of diatoms in ,river-reservoir hybrids' (i.e. rivers which have the characteristics of a reservoir during the dry season). This result provides specific information about the bloom of S. hantzschii in river systems, as well as the applicability of inductive methods, such as evolutionary computation to river-reservoir hybrid systems. [source] GENETIC PROGRAMMING AND ITS APPLICATION IN REAL-TIME RUNOFF FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2001Soon Thiam Khu ABSTRACT: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly. [source] An elaborate education of basic genetic programming using C++COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 3 2010Nirod C. Sahoo Abstract Evolutionary search is a global search method based on natural selection. In engineering curriculum, these techniques are taught in courses like Evolutionary Computation, Engineering Optimization, etc. Genetic algorithm (GA) is popular among these algorithms. Genetic programming (GP), developed by John Koza, is a powerful extension of GA where a chromosome/computer program (CP) is coded as a rooted point-labeled tree with ordered branches. The search space is the space of all possible CPs (trees) consisting of functions and terminals appropriate to the problem domain. GP uses, like GA, crossover and mutation for evolution. Due to tree-structured coding of individuals, the initial population generation, genetic operators' use, and tree decoding for fitness evaluations demand careful computer programming. This article describes the programming steps of GP implementation (using C++ language) for students' easy understanding with pseudocodes for each step. Two application examples are also illustrated. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 434,448, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20165 [source] A challenge for regenerative medicine: Proper genetic programming, not cellular mimicryDEVELOPMENTAL DYNAMICS, Issue 12 2007Angie Rizzino Abstract Recent progress in stem cell biology and the reprogramming of somatic cells to a pluripotent phenotype has generated a new wave of excitement in regenerative medicine. Nonetheless, efforts aimed at understanding transdifferentiation, dedifferentiation, and the plasticity of cells, as well as the ability of somatic cells to be reprogrammed, has raised as many questions as those that have been answered. This review proffers the argument that many reports of transdifferentiation, dedifferentiation, and unexpected stem cell plasticity may be due to aberrant processes that lead to cellular look-alikes (cellular mimicry). In most cases, cellular look-alikes can now be identified readily by monitoring gene expression profiles, as well as epigenetic modifications of DNA and histone proteins of the cells involved. This review further argues that progress in regenerative medicine will be significantly hampered by failing to address the issue of cellular look-alikes. Developmental Dynamics 236:3199,3207, 2007. © 2007 Wiley-Liss, Inc. [source] Action control of autonomous agents in continuous valued space using RFCNELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 2 2008Shinichi Shirakawa Abstract Researchers on action control of autonomous agents and multiple agents have attracted increasing attention in recent years. The general methods using action control of agents are neural network, genetic programming, and reinforcement learning. In this study, we use neural network for action control of autonomous agents. Our method determines the structure and parameter of neural network in evolution. We proposed Flexibly Connected Neural Network (FCN) previously as a method of constructing arbitrary neural networks with optimized structures and parameters to solve unknown problems. FCN was applied to action control of an autonomous agent and showed experimentally that it is effective for perceptual aliasing problems. All of the experiments of FCN, however, are only in grid space. In this paper, we propose a new method based on FCN which can decide correction action in real and continuous valued space. The proposed method, called Real-valued FCN (RFCN), optimizes input,output functions of each unit, parameters of the input,output functions and speed of each unit. In order to examine its effectiveness, we applied the proposed method to action control of an autonomous agent to solve continuous-valued maze problems. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 31,39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10032 [source] Virus-evolutionary linear genetic programmingELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 1 2008Kenji Tamura Abstract Many kinds of evolutionary methods have been proposed. GA and GP in particular have demonstrated their effectiveness in various problems recently, and many systems have been proposed. One is Virus-Evolutionary Genetic Algorithm (VE-GA), and the other is Linear Genetic Programming in C (LGPC). The performance of each system has been reported. VE-GA is the coevolution system of host individuals and virus individuals. That can spread schema effectively among the host individuals by using virus infection and virus incorporation. LGPC implements the GP by representing the individuals to one dimension as if GA. LGPC can reduce a search cost of pointer and save machine memory, and can reduce the time to implement GP programs. We have proposed that a system introduce virus individuals in LGPC, and analyzed the performance of the system on two problems. Our system can spread schema among the population, and search solution effectively. The results of computer simulation show that this system can search for solution depending on LGPC applying problem's character compared with LGPC. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 32, 39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10030 [source] Winter diatom blooms in a regulated river in South Korea: explanations based on evolutionary computationFRESHWATER BIOLOGY, Issue 10 2007DONG-KYUN KIM Summary 1. An ecological model was developed using genetic programming (GP) to predict the time-series dynamics of the diatom, Stephanodiscus hantzschii for the lower Nakdong River, South Korea. Eight years of weekly data showed the river to be hypertrophic (chl. a, 45.1 ± 4.19 ,g L,1, mean ± SE, n = 427), and S. hantzschii annually formed blooms during the winter to spring flow period (late November to March). 2. A simple non-linear equation was created to produce a 3-day sequential forecast of the species biovolume, by means of time series optimization genetic programming (TSOGP). Training data were used in conjunction with a GP algorithm utilizing 7 years of limnological variables (1995,2001). The model was validated by comparing its output with measurements for a specific year with severe blooms (1994). The model accurately predicted timing of the blooms although it slightly underestimated biovolume (training r2 = 0.70, test r2 = 0.78). The model consisted of the following variables: dam discharge and storage, water temperature, Secchi transparency, dissolved oxygen (DO), pH, evaporation and silica concentration. 3. The application of a five-way cross-validation test suggested that GP was capable of developing models whose input variables were similar, although the data are randomly used for training. The similarity of input variable selection was approximately 51% between the best model and the top 20 candidate models out of 150 in total (based on both Root Mean Squared Error and the determination coefficients for the test data). 4. Genetic programming was able to determine the ecological importance of different environmental variables affecting the diatoms. A series of sensitivity analyses showed that water temperature was the most sensitive parameter. In addition, the optimal equation was sensitive to DO, Secchi transparency, dam discharge and silica concentration. The analyses thus identified likely causes of the proliferation of diatoms in ,river-reservoir hybrids' (i.e. rivers which have the characteristics of a reservoir during the dry season). This result provides specific information about the bloom of S. hantzschii in river systems, as well as the applicability of inductive methods, such as evolutionary computation to river-reservoir hybrid systems. [source] A genetic-programming-based formulation for the strength enhancement of fiber-reinforced-polymer-confined concrete cylindersJOURNAL OF APPLIED POLYMER SCIENCE, Issue 5 2008Abdulkadir Cevik Abstract This study addresses the availability of the genetic programming (GP) approach for the formulation of strength enhancement of FRP (fiber-reinforced polymer) confined concrete cylinders. The GP formulation is based on experimental results collected from the literature. The accuracy of the proposed GP formulation was satisfactory compared to the experimental results. Moreover, the results of the proposed GP formulation were compared with 10 models from the literature proposed by various researchers so far and were found to be more accurate. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008 [source] Optimal control for linear system using genetic programmingOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2009A. Vincent Antony Kumar Abstract In this paper, optimal control for a linear system with quadratic performance is obtained using genetic programming (GP). The goal is to find the optimal control with reduced calculus effort using non-traditional methods. The obtained GP solution is compared with the traditional Runge,Kutta method. To obtain optimal control, the solution of matrix Riccati differential equation is computed based on grammatical evolution. The accuracy of the solution of the GP approach to the problem is qualitatively better than traditional methods. An illustrative numerical example is presented for the proposed method. Copyright © 2008 John Wiley & Sons, Ltd. [source] Predicting regularities in lattice constants of GdFeO3 -type perovskitesACTA CRYSTALLOGRAPHICA SECTION B, Issue 1 2008Asifullah Khan A novel idea of employing genetic programming to obtain mathematical expressions representing the dependency of lattice constants (LC) on their atomic parameters is presented in this paper. The results obtained from simulations reveal that only two atomic parameters are sufficient for LC prediction of GdFeO3 -type perovskites. In addition, an advantage of this approach is that there is no need to save any trained model as in the case of other existing machine-learning based approaches. [source] Time Series Modeling of Two- and Three-Phase Flow Boiling Systems with Genetic ProgrammingCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 11 2007M.-Y. Liu Abstract The time series of the physical parameters in boiling evaporators with vapor-liquid (V-L) two-phase and vapor-liquid-solid (V-L-S) three-phase external natural circulating flows exhibit nonlinear features. Hence, proper system evolution models may be built from the point of view of nonlinear dynamics. In this work, genetic programming (GP) was utilized to find the nonlinear modeling functions necessary to develop global explicit two-variable iteration models, using wall temperature signals measured from the heated tube in ordinary two-phase and three-phase fluidized bed evaporators. The model predictions agree well with the experimental data of the time series, which means that the models established with GP can adequately describe the dynamic evolution behavior of multi-phase flow boiling systems. [source] | ||||||||||||||||