Choosing transmitter locations among alternatives optimally in a radio network, regarding a known area, to guarantee a stipulated quality of service (QOS), is tackled by coarse-grained parallel genetic algorithms, whi...
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ISBN:
(纸本)0780372689
Choosing transmitter locations among alternatives optimally in a radio network, regarding a known area, to guarantee a stipulated quality of service (QOS), is tackled by coarse-grained parallel genetic algorithms, which maximize the coverage while reduce the number of utilized transmitters. An exclusive local search operator is raised, and the impacts of neighbor topologies are compared. Simulations on a dedicated cluster demonstrate that our local search operator is very effective, in the meantime, increasing the neighbor number of subpopulations will ameliorate optimization quality.
This paper proposes to apply coarse-grainedparallelgenetic algorithm (CGPGA) to solve polygonal approximation problem. Chromosomes are used to represent digital curves and genes correspond to points of curves. This ...
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This paper proposes to apply coarse-grainedparallelgenetic algorithm (CGPGA) to solve polygonal approximation problem. Chromosomes are used to represent digital curves and genes correspond to points of curves. This method divides the whole population into several subpopulations, each of which performs evolutionary process independently. After every migration interval number of generations, these subpopulations exchange their information with each other. Inspired by the designing theory of ensemble learning in machine learning, this paper further improves the basic CGPGA through adopting different but effective geneticalgorithms, respectively, in different subpopulations. Both the diversity among different subpopulations and the accuracy in each individual subpopulation are ensured. Experimental results, based on four benchmark curves and four real image curves extracted from the lake maps, show that the basic CGPGA outperforms the used genetic algorithm, and further the improved CGPGA (ICGPGA) is more effective than the basic CGPGA, in terms of the quality of best solutions, the average solutions, and the variance of best solutions. Especially for those larger approximation problems, the ICGPGA is more remarkably superior to some representative geneticalgorithms. (c) 2019 Elsevier Inc. All rights reserved.
When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identi...
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ISBN:
(纸本)9780819471529
When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic (HP) models. Distributed systems have become the focus of protein folding, providing high performance computing power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful information in distributed systems through careful consideration. Our study focuses on understanding the behavior of distributed systems and developing an efficient communication strategy to save computational effort in order to obtain good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.
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