摘要翻译:
提出了一种分布式遗传算法DAMS,用于分布式环境中的自适应优化。给定一组元启发式,DAMS的目标是协调它们在分布式节点上的局部执行,以优化分布式系统的全局性能。DAMS基于三层体系结构,允许节点在优化过程中分布式地决定传递哪些本地信息,以及应用哪些元启发式。大坝的适应性特征首先在一个非常普遍的环境中得到解决。然后,从并行和自适应的角度描述和分析了一个称为SBM的具体大坝。SBM是一种简单、高效、自适应的分布式算法,它使用一个利用组件允许节点选择具有最佳局部观察性能的元启发式,以及一个探索组件允许节点检测具有实际最佳性能的元启发式。通过实验以及与其他自适应策略(顺序和分布式)的比较,证明了BSM-DAMS的有效性。
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英文标题:
《DAMS: Distributed Adaptive Metaheuristic Selection》
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作者:
Bilel Derbel (LIFL, INRIA Lille - Nord Europe), S\'ebastien Verel
(INRIA Lille - Nord Europe)
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最新提交年份:
2012
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Neural and Evolutionary Computing 神经与进化计算
分类描述:Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络,连接主义,遗传算法,人工生命,自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
We present a distributed generic algorithm called DAMS dedicated to adaptive optimization in distributed environments. Given a set of metaheuristic, the goal of DAMS is to coordinate their local execution on distributed nodes in order to optimize the global performance of the distributed system. DAMS is based on three-layer architecture allowing node to decide distributively what local information to communicate, and what metaheuristic to apply while the optimization process is in progress. The adaptive features of DAMS are first addressed in a very general setting. A specific DAMS called SBM is then described and analyzed from both a parallel and an adaptive point of view. SBM is a simple, yet efficient, adaptive distributed algorithm using an exploitation component allowing nodes to select the metaheuristic with the best locally observed performance, and an exploration component allowing nodes to detect the metaheuristic with the actual best performance. The efficiency of BSM-DAMS is demonstrated through experimentations and comparisons with other adaptive strategies (sequential and distributed).
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PDF链接:
https://arxiv.org/pdf/1207.4448