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IEEE International Symposium on Intelligent Signal Processing

作者： Cruz-Roldan, Fernando Salcedo-Sanz, Sancho Portilla-Figueras, Jose Antonio Gimeno-Martinez, Nuria Univ Alcala de Henares Escuela Politecn Dept Teor Senal & Comun Madrid 28871 Spain

ISBN: (纸本)9781424408290

Recently, two evolutionary programming (EP) algorithms (Classical EP and Fast EP) have been proposed in order to design Finite Impulse Response digital filters [1]. The proposed techniques can be used to design digital fillers for a wide range of applications, such as data transmission, subband coding or narrowband interference detection. Here, we focus our attention on the design of nearly perfect reconstruction Cosine Modulated Filter Banks. The EP algorithms are used to determine the optimum values for the samples of the magnitude response Fourier transform. located in the transition hand of the prototype filter. Thus. the technique is simplified by constraining most the Fourier transform magnitude of the prototype filter., leaving only a small number of values to be optimized. The analytical and simulation results show again that the designed system performance is extremely good.

关键词： evolutionary programming channel bank filters cosine modulated filter bank nearly perfect reconstruction filtering theory

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An evolutionary programming based simulated annealing method for solving the unit commitment problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2007年第7期29卷 540-550页

作者： Christober Asir Rajan, C. Mohan, M. R. Pondicherry Engn Coll Dept EEE Pondicherry 605014 India Anna Univ Dept EEE Madras 600025 Tamil Nadu India

This paper presents a new approach to solve the short-term unit commitment problem using an evolutionary programming based simulated annealing method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. evolutionary programming, which happens to be a global optimisation technique for solving unit commitment Problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status ("flat start"). Here the parents are obtained from a pre-defined set of solution's, i.e. each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit's minimum down times. And SA improves the status. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different power systems consists of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like Dynamic programming, Lagrangian Relaxation and Simulated Annealing and Tabu Search in reaching proper unit commitment. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： unit commitment simulated annealing dynamic programming tabu search evolutionary programming

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Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

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SOFT COMPUTING 2007年第9期11卷 839-846页

作者： Cheong, France Lai, Richard RMIT Univ Melbourne Sch Business IT Melbourne Vic 3000 Australia La Trobe Univ Dept Comp Sci & Comp Engn Bundoora Vic 3083 Australia

With the availability of a wide range of evolutionary Algorithms such as Genetic Algorithms, evolutionary programming, evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.

关键词： evolutionary algorithms evolutionary programming fuzzy logic controller fuzzy systems

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An evolutionary programming tool for assessing the operational value of distributed energy resources within restructured electricity industries

An evolutionary programming tool for assessing the operation...

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Australasian Universities Power Engineering Conference (AUPEC)

作者： MacGill, I. F. Univ New S Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (纸本)9780646494883

There is growing world-wide and Australian interest in the greater potential role of distributed generation and demand-side resources within the electricity industry. These distributed resources can offer promising economic and environmental benefits for power system operation. There are considerable challenges, however, in developing modelling tools that can explore the operational value of such resources within restructured electricity industries. This paper describes a Dual evolutionary programming approach where software agents for power system resources co-evolve optimal operational behaviours over repeated power system simulations. The tool is applied to a simple case study exploring the potential operational synergies between significant PV penetrations and distributed energy storage options including controllable loads. The case study demonstrates this tool's capabilities in modelling the potentially complex operational behaviours of these distributed resources including stochastic PV outputs and loads with varying daily demand profiles, thermal energy storage, charging and discharging constraints and self-leakage.

关键词： distributed energy resources restructured electricity industries evolutionary programming power system simulation

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evolutionary Design of Wide-Band Parasitic Dipole Arrays

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2011年第11期59卷 4094-4102页

作者： Casula, Giovanni Andrea Mazzarella, Giuseppe Sirena, Nicola Dipartimento Ingn Elettr & Elettron I-09123 Cagliari Italy

An innovative antenna design technique, based on evolutionary programming, has been devised and applied to the design of broadband parasitic wire arrays for VHF-UHF bands with a significant gain. The chosen fitness function includes far-field requirements, as well as wideband input matching specifications. The latter requirements, which must be present in every useful antenna design, allow to stabilize the algorithm, and to design both optimal and robust antennas. The designed antennas show significant improvements over existing solutions (Yagi and LPDA) for the same frequency bands.

关键词： Automatic design evolutionary programming parasitic dipole array

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Power flow solvability identification and calculation algorithm using evolutionary programming technique

Power flow solvability identification and calculation algori...

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5th Student Conference on Research and Development

作者： Talib, Kahardi Muhammad Musirin, Ismail Kalil, Mohd. Rozely Idris, Mohamad Khayat IEEE Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9781424414697

Power flow study has been identified as the most important issue in power systems especially in the field of assessing the power system operability, survivability and also its security. It is considered as the back bone prior to further power system analysis, operation and planning. There have been various ready made products for power flow study packages in the market. Nonetheless, any attempts to solve power flow solution utilizing new developed algorithm or techniques can be considered as a brave trial. This paper proposes the evolutionary programming (EP) optimization technique to address the power flow problems through optimization technique. EP is based on the survivors of the fittest technique;where it is a sub-division of evolutionary Computation (EC) under the hierarchy of Artificial Intelligence (AI). Its capability in solving multi-variables, non-convex, non-linear and/or single or multi-objective optimization problems have been highlighted as the strength of EP. In realizing the effectiveness of EP in solving power problems, standard test system was utilized to ensure its workability in solving non-linear equations involving several pre-determined equality and inequality constraints equations. Results obtained from this study were compared with the existing established techniques;promising results were discovered implying that this technique is feasible to be implemented in addressing further optimization problems.

关键词： evolutionary programming power flow solution objective function optimization transmission loss

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The self-adaptive fuzzy neural network based on evolutionary programming

The self-adaptive fuzzy neural network based on evolutionary...

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6th International Conference on Machine Learning and Cybernetics

作者： Liu, Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing 100022 Peoples R China

ISBN: (纸本)9781424409723

In this paper, a approach for automatically generating fuzzy rules from sample patterns is presented. Firstly, with Cauchy mute operator and Gaussian mute operator, we propose a new evolutionary programming(EP) based on self-adaptive EP. Secondly, a self-adaptive fuzzy neural network is built based on the new evolutionary programming. In this method, structure identification and parameters estimation are performed automatically and simultaneously. The simulation results show that the proposed method in this paper can produce the compact and high performance fuzzy rule-base in comparison with other algorithms.

关键词： evolutionary programming fuzzy neural network

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A Hybrid evolutionary programming Algorithm for Spread Spectrum Radar Polyphase Codes Design 07

A Hybrid Evolutionary Programming Algorithm for Spread Spect...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Perez-Bellido, Angel M. Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Portilla-Figueras, Antonio Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain

ISBN: (纸本)9781595936974

This paper presents a hybrid evolutionary programming algorithm to solve the spread spectrum radar polyphase code design problem. The proposed algorithm uses an evolutionary programming (EP) approach as global search heuristic. This EP is hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perforin accurate and efficient local search for better solutions. Numerical examples demonstrate that the algorithm outperforms existing approaches for this problem.

关键词： Polyphase codes code design evolutionary programming hybrid algorithms

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A New Hybrid Algorithm for Finding the Lowest Minima of Potential Surfaces: Approach and Application to Peptides

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2011年第9期32卷 1785-1800页

作者： Goldstein, Moshe Fredj, Erick Gerber, R. Benny Hebrew Univ Jerusalem Fritz Haber Res Ctr Mol Dynam Inst Chem IL-91904 Jerusalem Israel JCT Dept Comp Sci IL-91160 Jerusalem Israel Univ Calif Irvine Dept Chem Irvine CA 92697 USA

A new algorithm is presented for finding the global minimum, and other low-lying minima, of a potential energy surface (PES) of biological molecules. The algorithm synergetically combines three well-known global optimization methods: the diffusion equation method (DEM), which involves smoothing the PES;a simulated annealing (SA) algorithm;and evolutionary programming (EP), whose population-oriented approach allows for a parallel search over different regions of the PES. Tests on five peptides having between 6 and 9 residues show that the code implementing the new combined algorithm is efficient and is found to outperform the constituent methods, DEM and SA. Results of the algorithm, in the gas phase and with the GBSA implicit solvent model, are compared with crystallographic data for the test peptides;good accord is found in all cases. Also, for all but one of the examples, our hybrid algorithm finds a minimum deeper than those obtained by a very extensive scan. TINKERs implementation of the OPLS-AA force field is employed for the structure prediction. The results show that the new algorithm is a powerful structure predictor, when a reliable potential function is available. Our implementation of the algorithm is time-efficient, and requires only modest computational resources. Work is underway on applications of the new algorithm to structural prediction of proteins and other biological macro-molecules. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 1785-1800, 2011

关键词： structure prediction diffusion equation method simulated annealing evolutionary programming cyclic peptides hybrid algorithm

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An improved genetic programming to SSM/I estimation typhoon precipitation over ocean

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HYDROLOGICAL PROCESSES 2011年第16期25卷 2573-2583页

作者： Chen, Li Yeh, Keh-Chia Wei, Hsiao-Ping Liu, Gin-Rong Chung Hua Univ Dept Civil Engn Hsinchu 30012 Taiwan Natl Chiao Tung Univ Dept Civil Engn Hsinchu 300 Taiwan Natl Sci & Technol Ctr Disaster Reduct Taipei 32001 Taiwan Natl Cent Univ Ctr Space & Remote Sensing Res Tao Yuan 32001 Taiwan

This article proposes an improved multi-run genetic programming (GP) and applies it to estimate the typhoon rainfall over ocean using multi-variable meteorological satellite data. GP is a well-known evolutionary programming and data mining method used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. However, the searching efficiency of traditional GP can be decreased by the complex structure of parse tree to represent the multiple input variables. This study processed an improvement to enhance escape ability from local optimums during the optimization procedure. We continuously run GP several times by replacing the terminal nodes at the next run with the best solution at the current run. The current method improves GP, obtaining a highly nonlinear meaningful equation to estimate the rainfall. In the case study, this improved GP (IGP) described above combined with special sensor microwave imager (SSM/I) seven channels was employed. These results are then verified with the data from four offshore rainfall stations located on islands around Taiwan. The results show that the IGP generates sophisticated and accurate multi-variable equation through two runs. The performance of IGP outperforms the traditional multiple linear regression, back-propagated network (BPN) and three empirical equations. Because the extremely high values of precipitation rate are quite few and the number of zero values (no rain) is very large, the underestimations of heavy rainfall are obvious. A simple genetic algorithm was therefore used to search for the optimal threshold value of SSM/I channels, detecting the data of no rain. The IGP with two runs, used to construct an appropriate mathematical function to estimate the precipitation, can obtain more favourable results from estimating extremely high values. Copyright. (C) 2011 John Wiley & Sons, Ltd.

关键词： genetic programming meteorological satellite SSM/I evolutionary programming data mining back-propagated network (BPN)

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