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MAPO: using a committee of algorithm-experts for parallel optimization of costly functions

Authors:

Christine A. Shoemaker,

Rommel G. RegisAuthors Info & Claims

SPAA '03: Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures

Pages 242 - 243

https://doi.org/10.1145/777412.777451

Published: 07 June 2003 Publication History

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Abstract

This paper describes a new parallel algorithm for optimizing costly nonconvex functions with box constraints when derivatives are unavailable. MAPO (Multi-Algorithm Parallel Optimization) iteratively uses a committee of optimization algorithms based on response surfaces to generate candidate points for function evaluation. In each iteration, the evaluation points are selected from multiple rankings of all candidate points. Good numerical results for MAPO with 4 radial basis function methods are reported for 8 processors.

References

[1]

L. C. W. Dixon and G. Szegö. The Global Optimization Problem: An Introduction. In Towards Global Optimization 2, pp. 1--15. North-Holland, Amsterdam, 1978.

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[2]

H.-M. Gutmann. A Radial Basis Function Method for Global Optimization. J. of Glob. Opt., 19(3): 201--227, 2001.

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[3]

F. Schoen. A Wide Class of Test Functions for Global Optimization. J. of Glob. Opt., 3: 133--138, 1993.

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MAPO: using a committee of algorithm-experts for parallel optimization of costly functions

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Published In

SPAA '03: Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures

June 2003

374 pages

ISBN:1581136617

DOI:10.1145/777412

General Chair:
Arnold Rosenberg
University of Massachusetts
,
Program Chair:
Friedhelm Meyer auf der Heide
U. Paderborn

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2003

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SPAA03

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SPAA03: 15th ACM Symposium on Parallelism in Algorithms and Architecturesn

June 7 - 9, 2003

California, San Diego, USA

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SPAA '03 Paper Acceptance Rate 38 of 106 submissions, 36%;

Overall Acceptance Rate 447 of 1,461 submissions, 31%

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