CONVERGENCE ANALYSIS OF STOCHASTIC DIFFUSION SEARCH
作者:
S. NASUTO,
M. BISHOP,
期刊:
Parallel Algorithms and Applications
(Taylor Available online 1999)
卷期:
Volume 14,
issue 2
页码: 89-107
ISSN:1063-7192
年代: 1999
DOI:10.1080/10637199808947380
出版商: Taylor & Francis Group
关键词: Probabilistic search;Best fit matching;Markov chain modeling;Distributed processing
数据来源: Taylor
摘要:
In this paper we present a connectionist searching technique - the Stochastic Diffusion Search (SDS), capable of rapidly locating a specified pattern in a noisy search space. In operation SDS finds the position of the pre-specified pattern or if it does not exist - its best instantiation in the search space. This is achieved via parallel exploration of the whole search space by an ensemble of agents searching in a competitive cooperative manner. We prove mathematically the convergence of stochastic diffusion search. SDS converges to a statistical equilibrium when it locates the best instantiation of the object in the search space. Experiments presented in this paper indicate the high robustness of SDS and show good scalability with problem size. The convergence characteristic of SDS makes it a fully adaptive algorithm and suggests applications in dynamically changing environments.
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