Approximate computing involves selectively reducing the number of transistors in a circuit to improve energy savings. Energy savings may be achieved at the cost of reduced accuracy for signal processing applications w...
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ISBN:
(纸本)9781467373005
Approximate computing involves selectively reducing the number of transistors in a circuit to improve energy savings. Energy savings may be achieved at the cost of reduced accuracy for signal processing applications whereby constituent adder and multiplier circuits need not generate a precise output. Since the performance versus energy savings landscape is complex, we investigate the acceleration of the design of approximate adders using parallelized geneticalgorithms (GAs). The fitness evaluation of each approximate adder is explored by the GA in a non-sequential fashion to automatically generate novel approximate designs within specified performance thresholds. This paper advances methods of parallelizing GAs and implements a new parallel GA approach for approximate multi-bit adder design. A speedup of approximately 1.6-fold is achieved using a quad-core Intel processor and results indicate that the proposed GA is able to find adders which consume 63.8% less energy than accurate adders.
The research based on complex RVRP. On the basis of deep analysis, a parallel genetic algorithm was designed. Simulation results proved that the parallel genetic algorithm is more excellent than conventional serial ge...
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ISBN:
(纸本)9783037858646
The research based on complex RVRP. On the basis of deep analysis, a parallel genetic algorithm was designed. Simulation results proved that the parallel genetic algorithm is more excellent than conventional serial geneticalgorithm.
geneticalgorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of the optimization tools used widely in solving problems based on natural selection and genetics. This paper is intend...
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ISBN:
(纸本)9781479915064;9781479915088
geneticalgorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of the optimization tools used widely in solving problems based on natural selection and genetics. This paper is intended to cover the study of GA and parallel GA and analyses its usage in CPU and GPU. One of the popular ways to speed up the processing time was by running them as parallel. The idea of parallel GAs may refer to an algorithm that works by dividing large problem into smaller tasks. Broad literature review in this paper includes a categorization of the GA operations that involved with some theories and techniques used in GA, presented with the aid of diagrams. This review attempts to study and analyse the behaviour of GA and parallel GA categories to work in GPU depending on the type of geneticalgorithm. parallel GA for GPU covers the architecture of Compute Unified Device Architecture (CUDA).
This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which th...
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This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to. slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.
This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of a...
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This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangements. Migration effects are evaluated from the viewpoints of scalability, chromosome diversity, migration frequency and solution quality. The results reveal that the performance of parallel GAs strongly depends on the design of the chromosome migration in which we need to consider the arrangement of heterogeneous computing resources, the migration frequency and so on. The results contribute to provide referential scheme of implementation of parallel GAs on heterogeneous computing resources.
A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PG...
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A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PGA is a feasible and effective optimization tool for inverse heat conduction problems.
Diversity technology can effectively resist channel multipath fading,and balanced technology can effectively inhibit the inter-symbol interference,so the diversity technology and balanced technology combined can effec...
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Diversity technology can effectively resist channel multipath fading,and balanced technology can effectively inhibit the inter-symbol interference,so the diversity technology and balanced technology combined can effectively improve the quality of ***,this paper uses parallel genetic algorithm to optimize the space diversity orthogonal wavelet adaptive algorithm,taking the space points on each branch equalizer weight vector as the son species of parallel genetic algorithms for selection,crossover,and mutation;between each species to each other and regularly send the best individual fitness;eliminate the worst individual fitness;and take diversity branch output signal and input orthogonal wavelet adaptive device *** computer simulation results show that the fast algorithm convergence speed and small steadystate errors can achieve the global optimal solution.
The aim of the work described in this paper is to investigate the implementation of Multi-core parallel genetic algorithm(McPGA) for real-time turning of PID parameters based on it using *** performance of our scheme ...
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The aim of the work described in this paper is to investigate the implementation of Multi-core parallel genetic algorithm(McPGA) for real-time turning of PID parameters based on it using *** performance of our scheme is discussed in comparison with that of the Sequential geneticalgorithm(SGA) program running on the same *** experimental results clearly show that McPGA is much better than SGA on convergence,premature and optimized *** can be used for PID parameter real-time turning in Industry Process Control Computer(IPC).
A new multi-objective evolutionary algorithm,called selective migration parallel genetic algorithm(SMPGA) was presented in this paper,which designs a new migration strategy and qualification based on the adaptive *** ...
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A new multi-objective evolutionary algorithm,called selective migration parallel genetic algorithm(SMPGA) was presented in this paper,which designs a new migration strategy and qualification based on the adaptive *** SMPGA,a searching population and a elite population evolve at the same time;unique migration strategy and qualification are used to keep and improve the convergence and diversity of the Pareto optimal ***,according to their different purposes,the two populations adopt different crossover *** results show that SMPGA can find accurate and uniform Pareto optimal solutions on different multi-objective problems.
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