abstract

Function evaluations upto 1e+12 and large population sizes assessed in distance-based success history differential evolution for 100-digit challenge and numerical optimization scenarios (DISHchain 1e+12): a competition entry for "100-digit challenge, and four other numerical optimization competitions" at the genetic and evolutionary computation conference (CECCO) 2019

Author:

Aleš ZamudaAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

Pages 11 - 12

https://doi.org/10.1145/3319619.3326751

Published: 13 July 2019 Publication History

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Abstract

This paper describes a competition entry for the "100-Digit Challenge, and Four Other Numerical Optimization Competitions" at The Genetic and Evolutionary Computation Conference (GECCO) 2019, by assessing the function evaluations up to 1e+12, and large population sizes in Distance-based Success History Differential Evolution for the 100-Digit Challenge and Numerical Optimization Scenarios (DISHchain 1e+12).

References

[1]

K. V. Price, N. H. Awad, M. Z. Ali, and P. N. Suganthan. 2018. The 100-Digit Challenge: Problem Definitions and Evaluation Criteria for the 100-Digit Challenge Special Session and Competition on Single Objective Numerical Optimization. Technical Report, Nanyang Technological University, Singapore, November 2018. Vacaville, California, USA and School of EEE, Nanyang Technological University, Singapore and School of Computer Information Systems, Jordan University of Science and Technology, Jordan.

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[2]

Adam Viktorin, Roman Senkerik, Michal Pluhacek, Tomas Kadavy, and Aleš Zamuda. Available online 12 November 2018. Distance Based Parameter Adaptation for Success-History based Differential Evolution. Swarm and Evolutionary Computation (Available online 12 November 2018).

Google Scholar

[3]

Aleš Zamuda and Janez Brest. 2018. On Tenfold Execution Time in Real World Optimization Problems with Differential Evolution in Perspective of Algorithm Design. In 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 1--5.

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[4]

Aleš Zamuda and José Daniel Hernández Sosa. 2019. Success history applied to expert system for underwater glider path planning using differential evolution. Expert Systems with Applications 119, 1 April 2019 (2019), 155--170.

Crossref

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[5]

A. Zamuda, J. D. Hernandez Sosa, and L. Adler. 2016. Improving Constrained Glider Trajectories for Ocean Eddy Border Sampling within Extended Mission Planning Time. In 2016 IEEE Congress on Evolutionary Computation. 1727--1734.

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Cited By

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Qian LKhishe MHuang YMirjalili S(2024)SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behaviorNeural Computing and Applications10.1007/s00521-023-09236-y36:9(4763-4786)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09236-y
Shen BKhishe MMirjalili S(2023)Evolving Marine Predators Algorithm by dynamic foraging strategy for real-world engineering optimization problemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106207123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106207
Sharma PRaju S(2023)Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functionsSoft Computing10.1007/s00500-023-09276-528:4(3123-3186)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s00500-023-09276-5
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Function evaluations upto 1e+12 and large population sizes assessed in distance-based success history differential evolution for 100-digit challenge and numerical optimization scenarios (DISHchain 1e+12): a competition entry for "100-digit challenge, and four other numerical optimization competitions" at the genetic and evolutionary computation conference (CECCO) 2019

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Information & Contributors

Information

Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Conference

GECCO '19

Sponsor:

SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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

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Cited By

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Qian LKhishe MHuang YMirjalili S(2024)SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behaviorNeural Computing and Applications10.1007/s00521-023-09236-y36:9(4763-4786)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09236-y
Shen BKhishe MMirjalili S(2023)Evolving Marine Predators Algorithm by dynamic foraging strategy for real-world engineering optimization problemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106207123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106207
Sharma PRaju S(2023)Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functionsSoft Computing10.1007/s00500-023-09276-528:4(3123-3186)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s00500-023-09276-5
Sun JLiu XBack TXu Z(2021)Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy GradientIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.306081125:4(666-680)Online publication date: Aug-2021
https://doi.org/10.1109/TEVC.2021.3060811
Wang JKhishe MKaveh MMohammadi H(2021)Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic for Solving Optimization ProblemsCognitive Computation10.1007/s12559-021-09933-7Online publication date: 13-Sep-2021
https://doi.org/10.1007/s12559-021-09933-7
Viktorin ASenkerik RPluhacek MKadavy TZamuda A(2020)DISH-XX Solving CEC2020 Single Objective Bound Constrained Numerical optimization Benchmark2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185633(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185633
Zamuda AAuger AStützle T(2019)Binary 100-digit challenge using IEEE-754 coded numerical optimization scenarios (100b-digit) and V-shape binary distance-based success history differential evolution (DISHv)Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326898(1821-1828)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326898

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