The Experts below are selected from a list of 9258 Experts worldwide ranked by ideXlab platform
J Yuryevich - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Programming based optimal power flow algorithm
IEEE Transactions on Power Systems, 1999Co-Authors: J Yuryevich, Kit Po WongAbstract:This paper develops an efficient and reliable Evolutionary Programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems, the algorithm is enhanced with gradient information. In the paper, the main elements of the Evolutionary Programming based OPF algorithm are presented. The algorithm is then demonstrated on the IEEE 30 bus test system.
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Evolutionary Programming based algorithm for environmentally constrained economic dispatch
IEEE Transactions on Power Systems, 1998Co-Authors: Kit Po Wang, J YuryevichAbstract:This paper develops an efficient and reliable Evolutionary-Programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load demand specifications in multiple intervals of the generation scheduling horizon. In the paper, the principal components of the Evolutionary-Programming-based ECED algorithm are presented. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a test system.
Kit Po Wong - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Programming based optimal power flow algorithm
IEEE Transactions on Power Systems, 1999Co-Authors: J Yuryevich, Kit Po WongAbstract:This paper develops an efficient and reliable Evolutionary Programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems, the algorithm is enhanced with gradient information. In the paper, the main elements of the Evolutionary Programming based OPF algorithm are presented. The algorithm is then demonstrated on the IEEE 30 bus test system.
David B. Fogel - One of the best experts on this subject based on the ideXlab platform.
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An Overview of Evolutionary Programming
Evolutionary Algorithms, 1999Co-Authors: David B. FogelAbstract:Evolutionary Programming is a method for simulating evolution that has been investigated for over 35 years. This paper offers an introduction to Evolutionary Programming, and indicates its relationship to other methods of Evolutionary computation, specifically genetic algorithms and evolution strategies. The original efforts that evolved finite state machines for predicting arbitrary time series, as well as specific recent efforts in combinatorial and continuous optimization, are reviewed. Some areas of current investigation are mentioned, including assessing the optimization performance of the technique and extensions to include mechanisms of self-adaptation.
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Evolutionary Programming - Reconstruction of DNA Sequence Information from a Simulated DNA Chip Using Evolutionary Programming
Evolutionary Programming, 1998Co-Authors: Gary B. Fogel, Kumar Chellapilla, David B. FogelAbstract:DNA sequencing methods are the subject of continued interest in molecular biology for use in a wide variety of applications. Sequencing DNA by hybridization on a “DNA chip” has been estimated to increase the rate of DNA sequencing by as much as one-million fold. In this process, the sequence of a target molecule is reconstructed by the complementary binding of a pool of random probe molecules. For each target, an appropriate probe length must be used to unambiguously determine the sequence of a given target sequence of length N. Using Evolutionary Programming, we have simulated the binding of probes of length four nucleotides to a series of target lengths to determine most optimal target length that can be unambiguously reconstructed. Evolutionary Programming is demonstrated to be well suited to sequence reconstruction problems and could also be extended for gene expression monitoring with DNA chip technology.
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Revisiting Evolutionary Programming
Proceedings of SPIE, 1998Co-Authors: David B. Fogel, Kumar ChellapillaAbstract:Evolutionary Programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measured by current standards. This paper provides a series of experiments that follow the framework of the original approach from the early 1960s, brought up to date with current computing machinery. A brief review of Evolutionary Programming and its relationship to other methods of Evolutionary computation, specifically genetic algorithms and evolution strategies, is also offered.
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Evolutionary Programming - Optimizing Fuel Distribution Through Evolutionary Programming
Evolutionary Programming, 1997Co-Authors: John R. Mcdonnell, W.c. Page, David B. Fogel, Lawrence J. FogelAbstract:Evolutionary Programming is demonstrated as a means for minimizing the cost of delivering fuel from a terminal to specified number of stations, each having a projected delivery window as well as carrier and shift constraints. The evolved solution compares favorably with the solution generated using the currently employed human-assisted optimizer. Evolutionary Programming offers the potential for considerable cost savings when applied to a large fleet of trucks and/or a large quantity of orders.
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Evolutionary Programming - Multi-operator Evolutionary Programming: A Preliminary Study on Function Optimization
Evolutionary Programming, 1997Co-Authors: N. Saravanan, David B. FogelAbstract:Classical Evolutionary Programming uses Gaussian mutation as the primary search operator. Recent studies have shown that using a Cauchy random variable as the primary operator leads to faster convergence for certain function optimization problems. In this study we explore the use of both the Gaussian and the Cauchy operators along with a self-adaptive mechanism to select the appropriate operator for each individual in the population. Empirical studies of the dual-operator Evolutionary Programming are conducted using a limited set of test function optimization problems.
Lawrence J. Fogel - One of the best experts on this subject based on the ideXlab platform.
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intelligence through simulated evolution forty years of Evolutionary Programming
1999Co-Authors: Lawrence J. FogelAbstract:Genesis motivation prediction experiments pattern recognition and classification control system design extension of early Evolutionary Programming concepts competitive goal-seeking some implications diversification two-person gaming against nonminimax players coevolution, pursuit and evasion modeling time series pattern recognition simulated ecosystems and the nature of intelligence sequence induction with deterministic automata revising and extending early Evolutionary Programming routing problems comparing crossover, inversion, and mutation specialisations finding structure in data self-adaptation evolving neural networks evolving S-expression and multiple interacting programs games other applications.
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Evolutionary Programming - Optimizing Fuel Distribution Through Evolutionary Programming
Evolutionary Programming, 1997Co-Authors: John R. Mcdonnell, W.c. Page, David B. Fogel, Lawrence J. FogelAbstract:Evolutionary Programming is demonstrated as a means for minimizing the cost of delivering fuel from a terminal to specified number of stations, each having a projected delivery window as well as carrier and shift constraints. The evolved solution compares favorably with the solution generated using the currently employed human-assisted optimizer. Evolutionary Programming offers the potential for considerable cost savings when applied to a large fleet of trucks and/or a large quantity of orders.
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Artificial Evolution - An Introduction to Evolutionary Programming
Lecture Notes in Computer Science, 1996Co-Authors: David B. Fogel, Lawrence J. FogelAbstract:Evolutionary Programming is a method for simulating evolution that has been investigated for over 30 years. This paper offers an introduction to Evolutionary Programming, and indicates its relationship to other methods of Evolutionary computation, specifically genetic algorithms and evolution strategies. The original efforts that evolved finite state machines for predicting arbitrary time series, as well as specific recent efforts in combinatorial and continuous optimization are reviewed. Some areas of current investigation are mentioned, including empirical assessment of the optimization performance of the technique and extensions of the method to include mechanisms to self-adapt to the error surface being searched.
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meta Evolutionary Programming
Asilomar Conference on Signals Systems and Computers, 1991Co-Authors: David B. Fogel, Lawrence J. Fogel, J W AtmarAbstract:A brief review of efforts is simulated evolution is given. Evolutionary Programming is a stochastic optimization technique that is useful for discovering the extrema of a nonlinear function. To implement such a search, several high-level parameters must be chosen, such as the amount of mutational noise, the severity of the mutation noise, and so forth. The authors address incorporating a meta-level Evolutionary Programming that can simultaneously evolve optimal settings for these parameters while a search for the appropriate extrema is being conducted. The preliminary experiments reported indicate the suitability of such a procedure. Meta-Evolutionary Programming was able to converge to points on each of two response surfaces that were close to the global optimum. >
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The Future of Evolutionary Programming
1990 Conference Record Twenty-Fourth Asilomar Conference on Signals Systems and Computers 1990., 1990Co-Authors: Lawrence J. FogelAbstract:Evolutionary Programming was conceived in I960 as an alternative approach to artificial intelligence. The initial applica+ tions concerned prediction, modeling, and the control of unknown processes with respect to an arbitrary payoff function. Future applications take the form of hierarchic Evolutionary Programming wherein higher levels evolve optinial parameters for lower levels, and artificial consciousness wherein a self-referential capability is used to enhance such hierarchic control. Some consideration is given to the generatioii ol true autonomy; that is, niachines that develop their own purpose. Some unanswered questions are offered for considera tion.
G. Mclennan - One of the best experts on this subject based on the ideXlab platform.
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Pulmonary CT image classification with Evolutionary Programming
Academic Radiology, 1999Co-Authors: M.t. Madsen, R. Uppaluri, E.a. Hoffman, G. MclennanAbstract:Rationale and Objectives. It is often difficult to classify information in medical images from derived features. The purpose of this research was to investigate the use of Evolutionary Programming as a tool for selecting important features and generating algorithms to classify computed tomographic (CT) images of the lung. Materials and Methods. Training and test sets consisting of 11 features derived from multiple lung CT images were generated, along with an indicator of the target area from which features originated. The images included five parameters based on histogram analysis, 11 parameters based on run length and co-occurrence matrix measures, and the fractal dimension. Two classification experiments were performed. In the first, the classification task was to distinguish between the subtle but known differences between anterior and posterior portions of transverse lung CT sections. The second classification task was to distinguish normal lung CT images from emphysematous images. The performance of the Evolutionary Programming approach was compared with that of three statistical classifiers that used the same training and test sets. Results. Evolutionary Programming produced solutions that compared favorably with those of the statistical classifiers. In separating the anterior from the posterior lung sections, the Evolutionary Programming results were better than two of the three statistical approaches. The Evolutionary Programming approach correctly identified all the normal and abnormal lung images and accomplished this by using less features than the best statistical method. Conclusion. The results of this study demonstrate the utility of Evolutionary Programming as a tool for developing classification algorithms.
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Pulmonary CT image classification using Evolutionary Programming
1997 IEEE Nuclear Science Symposium Conference Record, 1997Co-Authors: M.t. Madsen, R. Uppaluri, E.a. Hoffman, G. MclennanAbstract:The authors report on the use of Evolutionary Programming for classifying lung CT images. Evolutionary Programming uses a genetic algorithm to generate a complete, compilable program that optimizes a solution to set of training data, In this case, the training set consisted of 17 features derived from multiple lung CT images along with an indicator of the target area from which the features originated. The image features included 5 parameters based on histogram analysis, 11 parameters based on run length and co-occurrence matrix measures, and the fractal dimension. Evolutionary Programming produced solutions that compared favorably with more complicated and sophisticated Bayesian classifiers. The results of this study suggest that Evolutionary Programming is a powerful tool for developing classification algorithms.
