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A parallel genetic algorithm for cryptanalysis of the polyalphabetic substitution cipher

Cryptologia 1997年第2期21卷 129-138页

作者： Clark, Andrew Dawson, Ed Information Security Research Centre Queensland University of Technology Brisbane QLD 4001 GPO Box 2434 Australia

A new method for attacking the simple substitution cipher is presented which utilises a parallel version of the genetic algorithm. A suitable strategy is devised which allows communication between a number of parallel nodes each solving a separate part of the problem. An analysis of the fitness function is also performed. © 1997, United States Military Academy., All rights reserved.

关键词： Automated cryptanalysis parallel genetic algorithm Substitution cipher

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A parallel genetic algorithm for generation expansion planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 1996年第2期11卷 955-961页

作者： Fukuyama, Y Chiang, HD CORNELL UNIV SCH ELECT ENGNITHACANY 14853

This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.

关键词： generation expansion planning combinatorial optimization parallel genetic algorithm parallel computation multi-processors

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parallel genetic algorithm for service restoration in electric power distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1996年第2期18卷 111-119页

作者： Fukuyama, Y Chiang, HD Miu, KN su.edu Fuji Electric Corporate R & D Ltd No 1 Fuji-machi Hino-city Tokyo 191 Japan

This paper develops a coarse-grain parallel genetic algorithm for solving a service restoration problem in electric power distribution systems. Service restoration is performed to restore electricity for out-of-service areas. Developing effective service restoration procedures is a cost-effective approach to improving service reliability and enhancing customer satisfaction. The main objective in service restoration procedures is to restore as much load as possible by transferring de-energized loads via network reconfigurations to other supporting distribution feeders without violating operating and engineering constraints. Details of the parallel genetic algorithm developed in this paper are described The proposed method is implemented on transputers for parallel computations. The feasibility of the developed algorithm for service restoration is demonstrated on several distribution networks with promising results.

关键词： service restoration combinatorial optimization parallel genetic algorithm parallel computation multi-processors

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parallel genetic-based hybrid pattern matching algorithm for isolated word recognition

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 1998年第5期12卷 573-594页

作者： Kwong, S He, QH Man, KF Tang, KS Chau, CW City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China S China Univ China Hong Kong Peoples R China

Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path;and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a parallel genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner(23) is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.

关键词： speech recognition genetic algorithm parallel genetic algorithm dynamic time warping tree-trellis search

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genetic algorithm for optimizing the nonlinear time alignment of automatic speech recognition systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1996年第5期43卷 559-566页

作者： Kwong, S Chau, CW Halang, WA City University of Hong Kong Hong Kong China Hagen Germany

Dynamic time warping (DTW) is a nonlinear time-alignment technique for automatic speech recognition (ASR) systems, It had been widely used in many commercial and industrial products, ranging from electronic dailies/dictionaries to wireless voice digit dialers, DTW has the advantages of fast training and searching times, which makes it more popular than other available ASR techniques, However, there exist some limitations to DTW, such as the stringent rule on slope weighting, the nontrivial computation of the K-best paths, and the significant increase in computational time when the endpoint constraint is relaxed or the variations of the length of pattern increased, In this paper, a stochastic method called the genetic algorithm (GA), which is used to solve the nonlinear time alignment problem, is presented, Experimental results show that the GA has a better performance than the DTW, In addition, two derivatives of GA: the hybrid GA and the parallel GA are also presented.

关键词： dynamic time warping automatic speech recognition stochastic method genetic algorithm hybrid genetic algorithm parallel genetic algorithm

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Multistrategy learning using genetic algorithms and neural networks for pattern classification

Multistrategy learning using genetic algorithms and neural n...

引用

1996 IEEE International Conference on Systems, Man and Cybernetics - Information, Intelligence and Systems

作者： Zhou, YH Zhang, ZH Lu, YC Shi, CY Laboratory of Intelligent Technology& Systems Department of Computer Science Tsinghua University Beijing China

ISBN: (纸本)7800033813

This paper introduces a two-level learning algorithm which combines parallel genetic algorithm (PGA) and backpropagation algorithm (BP) in order to evolve optimal subsets of discriminatory features for robust pattern classification. In this approach, PGA is used to explore the space of all possible subsets of a large set of candidate discriminatory features. For a given subset, BP is invoked to be trained according to related training data. The individuals of population are evaluated by the classification performance of the trained BP according to the testing data. This process iterates until a satisfactory subset is attained. We use the classification of handwritten numeral and structure of ionosphere for experiment. The results show that this multistrategy methodology improves the classification accuracy rate and the speed of training.

关键词： neural network parallel genetic algorithm multistrategy learning pattern classification

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APPLICATION OF parallel genetic algorithmS TO GENERATION EXPANSION PLANNING USING parallel PROCESSORS

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ELECTRICAL ENGINEERING IN JAPAN 1995年第6期115卷 71-81页

作者： FUKUYAMA, Y UEKI, Y Fuji Electric Co. R&D Ltd

This paper presents an application of parallel genetic algorithms (PGA) to the optimal long-range generation expansion planning. The problem can be formulated as a combinatorial optimization problem that determines the order of introduced generation units at each interval of the year. The proposed method considers introduced power limits of each technology, maximum loads at each interval, and load duration curves at each interval. Appropriate string representation for the problem is presented. Binary and decimal coding and three selection methods are compared. The method is developed on a transputer that is one of the parallel processors. The feasibility of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals. The method is then compared with conventional dynamic programming and a simple genetic algorithm with promising results.

关键词： GENERATION EXPANSION PLANNING OPTIMAL GENERATION INTRODUCTION COMBINATORIAL OPTIMIZATION parallel genetic algorithm parallel PROCESSORS

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GUILLOTINEABLE BIN PACKING - A genetic APPROACH

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1995年第3期84卷 645-661页

作者： KROGER, B UNIV OSNABRUCK DEPT MATH & COMP SCID-40049 OSNABRUCKGERMANY

For a constrained, two-dimensional Bin Packing Problem this paper introduces a sequential and a parallel genetic algorithm. A guillotine constraint is directly reflected by the encoding mechanism and thus is ensured at any stage of the algorithm. As an enlargement, the concept of meta-rectangles is proposed and incorporated into the algorithm. Each meta-rectangle temporarily fixes (rectangular) hyperplanes of existing solutions. By this the hierarchical structure of guillotineable packing schemes is exploited to reduce the problem's complexity without affecting the quality of the generated solutions. The presented algorithm is able to generate almost optimal packing schemes and even in its sequential version the algorithm empirically is proven to be superior to different approaches like random search or simulated annealing.

关键词： 2-DIMENSIONAL BIN PACKING parallel genetic algorithm SIMULATED ANNEALING

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APPLYING genetic algorithmS TO FREQUENCY ASSIGNMENT PROBLEMS 3

APPLYING GENETIC ALGORITHMS TO FREQUENCY ASSIGNMENT PROBLEMS

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Conference on Neural and Stochastic Methods in Image and Signal Processing III

作者： CROMPTON, W HURLEY, S STEPHENS, NM UNIV WALES COLL CARDIFF DEPT COMP MATHCARDIFF CF24YNWALES

ISBN: (纸本)0819416282

This paper details the application of a parallel genetic algorithm to the air-ground-air frequency assignment problem. Preliminary results indicate that the technique is successful in finding acceptable assignments, satisfying over 90% of constraints, for realistically sized air- ground-air frequency assignment scenarios. Comparisons are made with a classical backtracking and forward checking heuristic algorithm which is shown to be inferior to the genetic algorithm in terms of the execution time required to find reasonable frequency assignments.

关键词： parallel genetic algorithm FREQUENCY ASSIGNMENT INTERFERENCE CONSTRAINTS NP-COMPLETE

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A SIMPLE parallelIZING SCHEME OF genetic algorithm ON DISTRIBUTED-MEMORY MULTIPROCESSORS

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International Journal of High Speed Computing 1994年第3期6卷 451-474页

作者： JONGHO NANG Department of Computer Science Sogang University 1 Shinsoo-Dong Mapo-Ku Seoul 121–742 Korea

This paper proposes a simple and efficient parallelizing scheme of the genetic algorithm on distributed-memory multiprocessor that maintains the execution behaviours of sequential genetic algorithm. In this parallelizing scheme, the global population is evenly partitioned into several subpopulations, each of which is assigned to the processor to be evolved in parallel. An interprocessor communication pattern, called AAB (All-To-All Broadcasting), is used at each generation in order to exchange the informations on all individuals evolved in all other processors. It allows the processor to reproduce the individuals in a global sense, in other words, the sequential execution behaviours can be maintained in the parallelized genetic algorithm. This paper shows that the genetic algorithms employing widely used selection schemes such as proportionate selection, ranking selection and tournament selection can be efficiently parallelized on distributed-memory multiprocessor using the proposed parallelizing scheme. Some experimental speedups on AP1000 are also presented to show the usefulness of the proposed parallelizing scheme.

关键词： genetic algorithm parallel processing parallel genetic algorithm preserving sequential behaviours multicomputers AP1000

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