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Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-before-detect systems

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SOFT COMPUTING 2013年第6期17卷 1095-1106页

作者： Mazurek, Przemyslaw West Pomeranian Univ Technol Dept Signal Proc & Multimedia Engn PL-71126 Szczecin Poland

Track-before-detect (TBD) algorithms are used for tracking systems, where the object's signal is below the noise floor (low-SNR objects). A lot of computations and memory transfers for real-time signal processing are necessary. GPGPU in parallel processing devices for TBD algorithms is well suited. Finding optimal or suboptimal code, due to lack of documentation for low-level programming of GPGPUs is not possible. High-level code optimization is necessary and the evolutionary approach, based on the single parent and single child is considered, that is local search approach. Brute force search technique is not feasible, because there are N! code variants, where N is the number of motion vectors components. The proposed evolutionary operator-LREI (local random extraction and insertion) allows source code reordering for the reduction of computation time due to better organization of memory transfer and the texture cache content. The starting point, based on the sorting and the minimal execution time metric is proposed. The unbiased random and biased sorting techniques are compared using experimental approach. Tests shows significant improvements of the computation speed, about 8 % over the conventional code for CUDA code. The time period of optimization for the sample code is about 1 h (1,000 iterations) for the considered recursive spatio-temporal TBD algorithm.

关键词： evolutionary programming Code optimization GPGPU Track-before-detect algorithm

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OPF-based reactive power planning and voltage stability limit improvement under single line outage contingency condition through evolutionary algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第4期21卷 1092-1106页

作者： Padaiyatchi, Sakthivel Daniel, Mary VRS Coll Engn & Technol Dept Elect & Elect Engn Villupuram TN India Govt Coll Engn Dept Elect & Elect Engn Bargur TN India

Reactive power planning is vital for maintaining the voltage stability of power systems and evolutionary algorithms are highly useful for achieving this task. This paper compares the effectiveness of the differential evolution (DE) and evolutionary programming (EP) algorithms in optimizing the reactive power planning of power systems under line outage contingency conditions. DE is efficient in exploration through the search space of the problem, while EP is simple and easy to implement. The low cost but fast response thyristor-controlled series capacitor (TCSC) flexible alternating current transmission system (FACTS) device is incorporated to control the power flows. The optimal settings of the control variables of the generator voltages, transformer tap settings, and location and parameter settings of the TCSC are considered for reactive power planning and the resultant reactive power reserves. The effectiveness of the proposed work is tested on the IEEE-30 Bus test system under the most critical line outage condition.

关键词： FACTS devices TCSC reactive power planning evolutionary programming differential evolution

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An interpretable classification rule mining algorithm

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INFORMATION SCIENCES 2013年 240卷 1-20页

作者： Cano, Alberto Zafra, Amelia Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

Obtaining comprehensible classifiers may be as important as achieving high accuracy in many real-life applications such as knowledge discovery tools and decision support systems. This paper introduces an efficient evolutionary programming algorithm for solving classification problems by means of very interpretable and comprehensible IF-THEN classification rules. This algorithm, called the Interpretable Classification Rule Mining (ICRM) algorithm, is designed to maximize the comprehensibility of the classifier by minimizing the number of rules and the number of conditions. The evolutionary process is conducted to construct classification rules using only relevant attributes, avoiding noisy and redundant data information. The algorithm is evaluated and compared to nine other well-known classification techniques in 35 varied application domains. Experimental results are validated using several non-parametric statistical tests applied on multiple classification and interpretability metrics. The experiments show that the proposal obtains good results, improving significantly the interpretability measures over the rest of the algorithms, while achieving competitive accuracy. This is a significant advantage over other algorithms as it allows to obtain an accurate and very comprehensible classifier quickly. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Classification evolutionary programming Interpretability Rule mining

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Reverse Engineering of Biochemical Reaction Networks Using Co-evolution with Eng-Genes

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COGNITIVE COMPUTATION 2013年第1期5卷 106-118页

作者： Gormley, Padhraig Li, Kang Wolkenhauer, Olaf Irwin, George W. Du, Dajun Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland Univ Rostock Dept Comp Sci Syst Biol & Bioinformat Grp D-18051 Rostock Germany Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

A major challenge when attempting to model biochemical reaction networks within the cell is that the dimensionality can become huge, where a large number of molecular species can be involved even in relatively small networks. This investigation attempts to infer models of these networks using a co-evolutionary algorithm that reverse engineers differential equation models of the target system from time-series data. The algorithm not only estimates the system parameters, but also the symbolic structure of the network. To reduce the problem of dimensionality, the algorithm uses a partitioning method while integrating candidate models in order to decouple system equations. In addition, the conventional evolutionary algorithm has been modified and extended to include a technique called 'eng-genes', where candidate models are built up from fundamental mathematical terms derived from knowledge about the target system a priori. This technique essentially focuses the search on more biologically plausible models. The approach is demonstrated on several example reaction networks. The results show that the eng-genes method of limiting the term pool using a priori knowledge improves the convergence of the reverse engineering process compared with the conventional method, resulting in more accurate and transparent models.

关键词： Reverse engineering System identification evolutionary programming Symbolic identification Parameter estimation Modelling

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An evolutionary algorithm with a history mechanism for tuning a chess evaluation function

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APPLIED SOFT COMPUTING 2013年第7期13卷 3234-3247页

作者： Vazquez-Fernandez, Eduardo Coello Coello, Carlos A. Sagols Troncoso, Feliu D. CINVESTAV IPN Dept Comp Sci Evolutionary Computat Grp Mexico City 07300 DF Mexico CINVESTAV IPN Dept Math Mexico City 07360 DF Mexico

Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the "good" virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory. (C) 2013 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithm evolutionary programming Chess engine Artificial intelligence Chess evaluation function

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A Wire Antenna for Broadband WLAN and Wi-Fi Applications

A Wire Antenna for Broadband WLAN and Wi-Fi Applications

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9th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Fanti, Alessandro Maxia, Paolo Musu, Claudia Univ Cagliari Dipartimento Ingn Elettr & Elettron Cagliari Italy

ISBN: (纸本)9781467324809

This paper presents a wire antenna for multi-band WLAN application, designed using the Structure-Based evolutionary programming, and having a very simple geometry. The antenna has been analysed with NEC-2 during the evolutionary process, and the outcome of the procedure shows a very good performance, with a -10dB bandwidth that covers the required frequencies for multi-band WLAN applications (2.4/5.2/5.8 GHz) and beyond, and an end-fire gain greater than 11 dB.

关键词： Wire Antennas evolutionary programming Wide Band Antennas

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A Wide Band Wire Antenna for WLAN and Wi-Fi Applications

A Wide Band Wire Antenna for WLAN and Wi-Fi Applications

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IEEE EUROCON Conference

作者： Casula, Giovanni Andrea Mazzarella, Giuseppe Montisci, Giorgio Fanti, Alessandro Maxia, Paolo Univ Cagliari Dipartimento Ingn Elettr & Elettron I-09123 Cagliari Italy

ISBN: (纸本)9781467322324;9781467322300

关键词： Wire Antennas evolutionary programming Wide Band Antennas

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evolutionary programming Incorporating Neural Network for Transient Stability Constrained Optimal Power Flow

Evolutionary Programming Incorporating Neural Network for Tr...

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Joint International Conference on Power Systems Technology (POWERCON)/IEEE Power India Conference

作者： Tangpatiphan, Kritsana Yokoyama, Akihiko Univ Tokyo Dept Elect Engn Tokyo 113 Japan

ISBN: (纸本)9781424417636

evolutionary programming incorporating neural network (EPNN) is proposed to obtain the solution of Transient Stability Constrained Optimal Power Flow (TSCOPF) in this paper. evolutionary programming (EP) is selected as a main optimizer while the neural network is a supplementary tool to enhance the computational speed by screening out individuals, which have very high or low degrees of stability. Swing equation and limit of rotor angle deviation with respect to centre of inertia (COI) are treated as additional constraints in transient stability concern. The generator fuel cost minimization is selected as the objective function for TSCOPF. The proposed method is tested on IEEE 30-bus system with three different generator cost curves to account for the combined cycle generating unit and valve point loading effect of a thermal generating unit. A three-phase fault at a specific transmission line is considered as a single contingency. The simulation results show that the proposed method is capable of searching for the optimal or near optimal solution of TSCOPF. Moreover, the exploitation of the neural network leads to huge computational time saving due to absence of time-domain simulation for some individuals during EP search.

关键词： evolutionary programming neural network optimal power flow transient stability

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Adaptive evolutionary programming based on reinforcement learning

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INFORMATION SCIENCES 2008年第4期178卷 971-984页

作者： Zhang, Huaxiang Lu, Jing Shandong Normal Univ Dept Comp Sci Jinan 250014 Shandong Peoples R China Shandong Univ Finance Dept Comp Sci Jinan 250014 Shandong Peoples R China

This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (evolutionary programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators. Mutation operator selection is mapped into a reinforcement learning problem. Reinforcement learning methods are used to learn optimal policies by maximizing the accumulated rewards. According to the calculated Q function value of each candidate mutation operator, an optimal mutation operator can be selected to maximize the learned Q function value. Four different mutation operators have been employed as the basic candidate operators in RLEP and one is selected for each individual in different generations. Our simulation shows the performance of RLEP is the same as or better than the best of the four basic mutation operators. (c) 2007 Elsevier Inc. All rights reserved.

关键词： reinforcement learning evolutionary programming mutation Q value

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An experimental study on the self-adaption mechanism used by evolutionary programming

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Progress in Natural Science:Materials International 2008年第4期18卷 481-486页

作者： Jingsong He a,b, a Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China b Nature Inspired Computation and Application Laboratory, University of Science and Technology of China, Hefei 230027, China Department of Electronic Science and Technology University of Science and Technology of China Hefei 230027 China Nature Inspired Computation and Application Laboratory University of Science and Technology of China Hefei 230027 China

evolutionary programming (EP) is one of the most important methods for numerical optimization. Its main technique is the combi- nation of mutations and the self-adaption mechanism. In the past years, studies on EP focused on how to improve the effciency of muta- tions with different probability distributions, and few of them touched on the question that the self-adaption mechanism did not work sometimes, but simply followed the original suggestion. So far, no experimental results have shown why this is a question. This paper firstly gives a primary analysis on the behavior of the self-adaption mechanism, and then presents experimental evidences to show why its adaptive ability is doubtful.

关键词： evolutionary programming Cauchy mutation Self-adaption mechanism

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