technical-note

Application of a simple binary genetic algorithm to a noiseless testbed benchmark

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2473 - 2478

https://doi.org/10.1145/1570256.1570346

Published: 08 July 2009 Publication History

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Abstract

One of the earliest evolutionary computation algorithms, the genetic algorithm, is applied to the noise-free BBOB 2009 testbed. It is adapted to the continuous domain by increasing the number of bits encoding each variable, until a desired resolution is possible to achieve. Good results and scaling are obtained for separable functions, but poor performance is achieved on the other functions, particularly ill-conditioned functions. Overall running times remain fast throughout.

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Felix-Saul JGarcía-Valdez MMerelo Guervós JCastillo O(2024)Extending Genetic Algorithms with Biological Life-Cycle DynamicsBiomimetics10.3390/biomimetics90804769:8(476)Online publication date: 6-Aug-2024
https://doi.org/10.3390/biomimetics9080476
García-Valdez MMerelo J(2021)Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary StrategiesApplications of Evolutionary Computation10.1007/978-3-030-72699-7_47(747-762)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_47
García Valdez MMerelo Guervós J(2020)A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithmsFuture Generation Computer Systems10.1016/j.future.2020.10.039Online publication date: Nov-2020
https://doi.org/10.1016/j.future.2020.10.039
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Index Terms

Application of a simple binary genetic algorithm to a noiseless testbed benchmark
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
      1. Computations on polynomials
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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Technical-note

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Cited By

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Felix-Saul JGarcía-Valdez MMerelo Guervós JCastillo O(2024)Extending Genetic Algorithms with Biological Life-Cycle DynamicsBiomimetics10.3390/biomimetics90804769:8(476)Online publication date: 6-Aug-2024
https://doi.org/10.3390/biomimetics9080476
García-Valdez MMerelo J(2021)Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary StrategiesApplications of Evolutionary Computation10.1007/978-3-030-72699-7_47(747-762)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_47
García Valdez MMerelo Guervós J(2020)A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithmsFuture Generation Computer Systems10.1016/j.future.2020.10.039Online publication date: Nov-2020
https://doi.org/10.1016/j.future.2020.10.039
Lavinas YAranha CSakurai TLadeira M(2018)Experimental Analysis of the Tournament Size on Genetic Algorithms2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00617(3647-3653)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00617
García-Valdez MMerelo J(2017)Benchmarking a pool-based execution with GA and PSO workers on the BBOB noiseless testbedProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3086573(1750-1755)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3086573
Nesmachnow SLuna FAlba E(2015)An empirical time analysis of evolutionary algorithms as C programsSoftware—Practice & Experience10.1002/spe.221745:1(111-142)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/spe.2217
Sawyerr BAdewumi AAli MAlba E(2013)Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbedProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482698(1193-1200)Online publication date: 6-Jul-2013
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Liao TMolina DStutzle TMontes de Oca MDorigo MMoore J(2012)An ACO algorithm benchmarked on the BBOB noiseless function testbedProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330809(159-166)Online publication date: 7-Jul-2012
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Hansen NAuger ARos RFinck SPošík PPelikan MBranke J(2010)Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009Proceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830790(1689-1696)Online publication date: 7-Jul-2010
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