A new method for attacking the simple substitution cipher is presented which utilises a parallel version of the geneticalgorithm. A suitable strategy is devised which allows communication between a number of parallel...
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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 new...
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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 geneticalgorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.
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-servic...
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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.
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 appl...
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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 parallelgenetic 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.
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/di...
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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 geneticalgorithm (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.
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 ...
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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.
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 th...
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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 geneticalgorithm with promising results.
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 a...
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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.
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, s...
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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 geneticalgorithm in terms of the execution time required to find reasonable frequency assignments.
This paper proposes a simple and efficient parallelizing scheme of the geneticalgorithm on distributed-memory multiprocessor that maintains the execution behaviours of sequential geneticalgorithm. In this paralleliz...
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This paper proposes a simple and efficient parallelizing scheme of the geneticalgorithm on distributed-memory multiprocessor that maintains the execution behaviours of sequential geneticalgorithm. 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 geneticalgorithm. This paper shows that the geneticalgorithms 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.
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