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Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed

Authors:

Antonio LaTorre,

Santiago Muelas,

José María PeñaAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

Pages 1725 - 1730

https://doi.org/10.1145/1830761.1830795

Published: 07 July 2010 Publication History

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Abstract

In this paper, a hybrid algorithm based on the Multiple Offspring Sampling framework is presented and benchmarked on the BBOB-2010 noisy testbed. MOS allows the seamless combination of multiple metaheuristics in a hybrid algorithm capable of dynamically adjusting the participation of each of the composing algorithms. The experimental results show a good performance on functions with moderate noise. However, on functions with severe noise the results deteriorate, which suggests that further research should be conducted to find more adequate control mechanisms for these types of functions.

References

[1]

A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population size. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, pages 1769--1776. IEEE Press, 2005.

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S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter Black-Box Optimization Benchmarking 2010: Presentation of the noisy functions. Technical Report 2009/21, Research Center PPE, 2010.

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N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter Black-Box Optimization Benchmarking 2010: Experimental setup. Technical Report RR-7215, INRIA, 2010.

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N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter Black-Box Optimization Benchmarking 2009: Noisy functions definitions. Technical Report RR-6869, INRIA, 2009. Updated February 2010.

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A. LaTorre. A Framework for Hybrid Dynamic Evolutionary Algorithms: Multiple Offspring Sampling (MOS). PhD thesis, Universidad Politécnica de Madrid, November 2009.

Google Scholar

[6]

A. LaTorre, S. Muelas, and J. Peña. Benchmarking a MOS-based algorithm on the BBOB-2010 noiseless function testbed. In Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference. ACM, 2010.

Digital Library

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R. Storn and K. Price. Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, 1995.

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Cited By

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Li ZZhang SCai XZhang QZhu XFan ZJia X(2022)Noisy Optimization by Evolution Strategies With Online Population Size LearningIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.313148252:9(5816-5828)Online publication date: Sep-2022
https://doi.org/10.1109/TSMC.2021.3131482
Astete-Morales SCauwet MTeytaud ONeumann FSutton A(2016)Analysis of Different Types of Regret in Continuous Noisy OptimizationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908933(205-212)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908933

Index Terms

Benchmarking a MOS-based algorithm on the BBOB-2010 noisy function testbed
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

July 2010

1496 pages

ISBN:9781450300735

DOI:10.1145/1830761

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2010

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GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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Li ZZhang SCai XZhang QZhu XFan ZJia X(2022)Noisy Optimization by Evolution Strategies With Online Population Size LearningIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.313148252:9(5816-5828)Online publication date: Sep-2022
https://doi.org/10.1109/TSMC.2021.3131482
Astete-Morales SCauwet MTeytaud ONeumann FSutton A(2016)Analysis of Different Types of Regret in Continuous Noisy OptimizationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908933(205-212)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908933

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JADE, an adaptive differential evolution algorithm, benchmarked on the BBOB noiseless testbed

Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed

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