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Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite

Published: 20 July 2016 Publication History

Abstract

The unbounded population multi-objective covariance matrix adaptation evolution strategy~(UP-MO-CMA-ES) aims at maximizing the total hypervolume covered by all evaluated points. It adds all non-dominated solutions found to its population and employs Gaussian mutations with adaptive covariance matrices to also solve ill-conditioned problems. A novel recombination operator adapts the covariance matrices to point along the Pareto front. The UP-MO-CMA-ES is combined with a parallel exploration strategy and empirically evaluated on the bi-objective BBOB-biobj benchmark problems. Results show that the algorithm can reliably solve ill-conditioned problems as well as weakly-structured problems. However, it is less suited for the rugged multi-modal objective functions in the benchmark.

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cover image ACM Conferences
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
July 2016
1510 pages
ISBN:9781450343237
DOI:10.1145/2908961
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 20 July 2016

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Author Tags

  1. benchmarking
  2. bi-objective optimization
  3. black-box optimization
  4. covariance matrix adaptation
  5. global optimization

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  • Research-article

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  • Personalized Breast Cancer Screening
  • Danish Center for Big Data Analytics Driven Innovation

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GECCO '16
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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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  • (2022)A Rough-to-Fine Evolutionary Multiobjective Optimization AlgorithmIEEE Transactions on Cybernetics10.1109/TCYB.2021.308135752:12(13472-13485)Online publication date: Dec-2022
  • (2021)Is Our Archiving Reliable? Multiobjective Archiving Methods on “Simple” Artificial Input SequencesACM Transactions on Evolutionary Learning and Optimization10.1145/34653351:3(1-19)Online publication date: 18-Aug-2021
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  • (2019)Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbedProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326896(1905-1911)Online publication date: 13-Jul-2019
  • (2019)Benchmarking MO-CMA-ES and COMO-CMA-ES on the bi-objective bbob-biobj testbedProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326892(1920-1927)Online publication date: 13-Jul-2019
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  • (2019)Impact of Estimation Method of Ideal/Nadir Points on Practically-Constrained Multi-Objective Optimization Problems for Decomposition-Based Multi-Objective Evolutionary Algorithm2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002760(2138-2145)Online publication date: Dec-2019
  • (2019)Coverage Enhancement of MOEA/D-M2M for Problems with Difficult-to-Approximate Pareto Front Boundaries2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790146(1734-1741)Online publication date: Jun-2019
  • (2018)GECCO 2018 tutorial on evolutionary multiobjective optimizationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207864(349-372)Online publication date: 6-Jul-2018
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