BibSonomy publications for /concept/tag/OperatorThu Jun 19 17:46:40 CEST 2008New YorkGECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference9-13 July853--860Using Schema Theory To Explore Interactions Of Multiple Operators2002interaction, length schema genetic bias, variable algorithms, linear operator proportion, theory, programming, structures In the last two years the schema theory for Genetic Programming (GP) has been applied to the problem of understanding the length biases of a variety of crossover and mutation operators on variable length linear structures. In these initial papers, operators were studied in isolation. In practice, however, they are typically used in various combinations, and in this paper we present the first schema theory analysis of the complex interactions of multiple operators. In particular, we apply the schema theory to the use of standard subtree crossover, full mutation, and grow mutation (in varying proportions) to variable length linear structures in the one-then-zeros problem. We then show how the results can be used to guide choices about the relative proportion of these operators in order to achieve certain structural goals during a run.Thu Jun 19 17:46:40 CEST 2008COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, KoreaProceedings of the 2001 Congress on Evolutionary Computation CEC200127-30 May1062--1069Exact {GP} Schema Theory for Headless Chicken Crossover and Subtree Mutation2001subtree biases headless algorithms, programming, mutation, schema crossover, theory, operator genetic chicken Thu Jun 19 17:46:40 CEST 2008IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews3287--300Fingerprint classification based on learned features35Augmethod, discovery, algorithm, feature Bayesian image database, methods, programming, NIST-4 Bayes genetic feature-learning databases operator extraction, learning algorithms, classifier, identification, classification fingerprint processing operations (artificial classification, primitive visual intelligence), composite In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3percent and 91.6percent, respectively, which compare favourably with other published research and are one of the best results published to date.Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Systems, Man and Cybernetics -- Part B: CyberneticsApril2351--357A two-level hybrid evolutionary algorithm for modeling one-dimensional dynamic systems by higher-order {ODE} models402000operator differential computation, THEMA, genetic systems, models, algorithms, crossover ODE one-dimensional equation, two-level dynamic evolutionary programming, algorithm, hybrid modeling ordinary This paper presents a new algorithm for modeling one-dimensional (1-D) dynamic systems by higher-order ordinary differential equation (HODE) models instead of the ARMA models as used in traditional time series analysis. A two-level hybrid evolutionary modeling algorithm (THEMA) is used to approach the modeling problem of HODE's for dynamic systems. The main idea of this modeling algorithm is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model (the upper level), while a GA is employed to optimize the parameters of the model (the lower level). In the GA, we use a novel crossover operator based on a nonconvex linear combination of multiple parents which works efficiently and quickly in parameter optimization tasks. Two practical examples of time series are used to demonstrate the THEMA's effectiveness and advantages.Thu Jun 19 17:35:00 CEST 2008Journal of Sound and Vibration30 May1-2213--226Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming2932006diagnosis, operator algorithms, genetic Fault programming, Engine valve, Immigration Available machine fault diagnostic methods show unsatisfactory performances on both on-line and intelligent analyses because their operations involve intensive calculations and are labour intensive. Aiming at improving this situation, this paper describes the development of an intelligent approach by using the Genetic Programming (abbreviated as GP) method. Attributed to the simple calculation of the mathematical model being constructed, different kinds of machine faults may be diagnosed correctly and quickly. Moreover, human input is significantly reduced in the process of fault diagnosis. The effectiveness of the proposed strategy is validated by an illustrative example, in which three kinds of valve states inherent in a six-cylinders/four-stroke cycle diesel engine, i.e. normal condition, valve-tappet clearance and gas leakage faults, are identified. In the example, 22 mathematical functions have been specially designed and 8 easily obtained signal features are used to construct the diagnostic model. Different from existing GPs, the diagnostic tree used in the algorithm is constructed in an intelligent way by applying a power-weight coefficient to each feature. The power-weight coefficients vary adaptively between 0 and 1 during the evolutionary process. Moreover, different evolutionary strategies are employed, respectively for selecting the diagnostic features and functions, so that the mathematical functions are sufficiently and in the meantime, the repeated use of signal features may be fully avoided. The experimental results are illustrated diagrammatically in the following sections.Wed Apr 23 22:05:04 CEST 2008Signal ProcessingAug81469--1487Hybrid human-machine binary morphological operator design. 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