Kocsis, Levente and György, András (2009) Efficient multi-start strategies for local search algorithms. In: ECML PKDD 2009. Machine learning and knowledge discovery in databases. European conference. Bled, 2009. (Lecture notes in computer science 5781.).

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Abstract

Local search algorithms for global optimization often suffer from getting trapped in a local optimum. The common solution for this problem is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behavior. Hence, a multi-start strategy has to decide (dynamically) when to allocate additional resources to a particular instance and when to start new instances. In this paper we propose a consistent multi-start strategy that assumes a convergence rate of the local search algorithm up to an unknown constant, and in every phase gives preference to those instances that could converge to the best value for a particular range of the constant. Combined with the local search algorithm SPSA (Simultaneous Perturbation Stochastic Approximation), the strategy performs remarkably well in practice, both on synthetic tasks and on tuning the parameters of learning algorithms.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Depositing User:	Eszter Nagy
Date Deposited:	11 Dec 2012 16:05
Last Modified:	11 Dec 2012 16:05
URI:	https://eprints.sztaki.hu/id/eprint/5974

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