Adaptive online time allocation to search algorithms

Matteo Gagliolo; Viktor Zhumatiy; Jürgen Schmidhuber

doi:10.1007/978-3-540-30115-8_15

Adaptive online time allocation to search algorithms

Matteo Gagliolo, Viktor Zhumatiy, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

Abstract

Given is a search problem or a sequence of search problems, as well as a set of potentially useful search algorithms. We propose a general framework for online allocation of computation time to search algorithms based on experience with their performance so far. In an example instantiation, we use simple linear extrapolation of performance for allocating time to various simultaneously running genetic algorithms characterized by different parameter values. Despite the large number of searchers tested in parallel, on various tasks this rather general approach compares favorably to a more specialized state-of-the-art heuristic; in one case it is nearly two orders of magnitude faster. © Springer-Verlag Berlin Heidelberg 2004.

Original language	English (US)
Title of host publication	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publisher	Springer Verlag
Pages	134-143
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-30115-8_15
State	Published - Jan 1 2004
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-30115-8_15

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title = "Adaptive online time allocation to search algorithms",

abstract = "Given is a search problem or a sequence of search problems, as well as a set of potentially useful search algorithms. We propose a general framework for online allocation of computation time to search algorithms based on experience with their performance so far. In an example instantiation, we use simple linear extrapolation of performance for allocating time to various simultaneously running genetic algorithms characterized by different parameter values. Despite the large number of searchers tested in parallel, on various tasks this rather general approach compares favorably to a more specialized state-of-the-art heuristic; in one case it is nearly two orders of magnitude faster. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.",

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AU - Gagliolo, Matteo

AU - Zhumatiy, Viktor

AU - Schmidhuber, Jürgen

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PY - 2004/1/1

Y1 - 2004/1/1

N2 - Given is a search problem or a sequence of search problems, as well as a set of potentially useful search algorithms. We propose a general framework for online allocation of computation time to search algorithms based on experience with their performance so far. In an example instantiation, we use simple linear extrapolation of performance for allocating time to various simultaneously running genetic algorithms characterized by different parameter values. Despite the large number of searchers tested in parallel, on various tasks this rather general approach compares favorably to a more specialized state-of-the-art heuristic; in one case it is nearly two orders of magnitude faster. © Springer-Verlag Berlin Heidelberg 2004.

AB - Given is a search problem or a sequence of search problems, as well as a set of potentially useful search algorithms. We propose a general framework for online allocation of computation time to search algorithms based on experience with their performance so far. In an example instantiation, we use simple linear extrapolation of performance for allocating time to various simultaneously running genetic algorithms characterized by different parameter values. Despite the large number of searchers tested in parallel, on various tasks this rather general approach compares favorably to a more specialized state-of-the-art heuristic; in one case it is nearly two orders of magnitude faster. © Springer-Verlag Berlin Heidelberg 2004.

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DO - 10.1007/978-3-540-30115-8_15

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SP - 134

EP - 143

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

PB - Springer Verlag

ER -

Adaptive online time allocation to search algorithms

Abstract

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