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Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed

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Tobias WagnerAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 1217 - 1223

https://doi.org/10.1145/2908961.2931704

Published: 20 July 2016 Publication History

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Abstract

The Comparing Continuous Optimizers platform COCO has become a standard for benchmarking numerical (single-objective) optimization algorithms effortlessly. In 2016, COCO has been extended towards multi-objective optimization by providing a first bi-objective test suite. To provide a baseline, we benchmark a pure random search on this bi-objective family bbob-biobj test suite of the COCO platform. For each combination of function, dimension n, and instance of the test suite, 10⁶ ⋅ n candidate solutions are sampled uniformly within the sampling box [-5,5]ⁿ.

References

[1]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. The impact of search volume on the performance of RANDOMSEARCH on the bi-objective BBOB-2016 test suite. In GECCO (Companion), 2016. to appear.

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A. Auger and R. Ros. Benchmarking the pure random search on the BBOB-2009 testbed. In F. Rothlauf, editor, GECCO (Companion), pages 2479--2484. ACM, 2009.

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D. Brockhoff, T. Tušar, D. Tušar, T. Wagner, N. Hansen, and A. Auger. Biobjective performance assessment with the COCO platform. ArXiv e-prints, arXiv:1605.01746, 2016.

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N. Hansen, A. Auger, D. Brockhoff, D. Tušar, and T. Tušar. COCO: Performance assessment. ArXiv e-prints, arXiv:1605.03560, 2016.

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N. Hansen, T. Tu\v sar, O. Mersmann, A. Auger, and D. Brockhoff. COCO: The experimental procedure. ArXiv e-prints, arXiv:1603.08776, 2016.

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Brockhoff DAuger AHansen NTušar T(2017)Quantitative Performance Assessment of Multiobjective Optimizers9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 1017310.1007/978-3-319-54157-0_8(103-119)Online publication date: 19-Mar-2017
https://dl.acm.org/doi/10.1007/978-3-319-54157-0_8
Auger ABrockhoff DHansen NTušar DTušar TWagner TNeumann FSutton A(2016)The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test SuiteProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931705(1225-1232)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2931705

Index Terms

Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed
1. Applied computing
  1. Operations research
    1. Decision analysis
      1. Multi-criterion optimization and decision-making
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization

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Published In

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

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Published: 20 July 2016

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Brockhoff DAuger AHansen NTušar T(2017)Quantitative Performance Assessment of Multiobjective Optimizers9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 1017310.1007/978-3-319-54157-0_8(103-119)Online publication date: 19-Mar-2017
https://dl.acm.org/doi/10.1007/978-3-319-54157-0_8
Auger ABrockhoff DHansen NTušar DTušar TWagner TNeumann FSutton A(2016)The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test SuiteProceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion10.1145/2908961.2931705(1225-1232)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908961.2931705

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Benchmarking MO-CMA-ES and COMO-CMA-ES on the bi-objective bbob-biobj testbed

Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite

Benchmarking MATLAB's gamultiobj (NSGA-II) on the Bi-objective BBOB-2016 Test Suite

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