Convergence of the Stochastic Diffusion Search

Nasuto, S. and Bishop, Mark (J. M.). 1999. Convergence of the Stochastic Diffusion Search. Parallel Algorithms, 14(2), pp. 89-107. [Article]

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Abstract or Description

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 prespecified 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 characteristics of SDS makes it a fully adaptive algorithm and suggests applications in dynamically changing environments.

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Date Deposited:

01 Dec 2015 12:22

Last Modified:

20 Jun 2017 09:39

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Yes, this version has been peer-reviewed.


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