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Selecting miners within blockchain-based systems using evolutionary algorithms for energy optimisation

Authors:

Akram Alofi,

Mahmoud A. Bokhari,

Robert Hendley,

Rami BahsoonAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 291 - 292

https://doi.org/10.1145/3449726.3459558

Published: 08 July 2021 Publication History

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Abstract

In this paper, we represent the problem of selecting miners within a blockchain-based system as a subset selection problem. We formulate the problem of minimising blockchain energy consumption as an optimisation problem with two conflicting objectives: energy consumption and trust. The proposed model is compared across different algorithms to demonstrate its performance.

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Cited By

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Deshmukh ASheth HPawar PTyagi A(2023)A Survey on Blockchain and Cryptocurrency-Based SystemsHandbook of Research on Blockchain Technology and the Digitalization of the Supply Chain10.4018/978-1-6684-7455-6.ch018(364-397)Online publication date: 21-Apr-2023
https://doi.org/10.4018/978-1-6684-7455-6.ch018
Alrumayh OAlmutairi A(2023)Novel secured distributed energy management structure for solar based smart grids incorporating minersSolar Energy10.1016/j.solener.2023.01.012251(134-145)Online publication date: Feb-2023
https://doi.org/10.1016/j.solener.2023.01.012
Alofi ABokhari MBahsoon RHendley R(2022)Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem FormulationIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31604917:4(910-922)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3160491
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Index Terms

Selecting miners within blockchain-based systems using evolutionary algorithms for energy optimisation
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software architectures
        Peer-to-peer architectures

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Published In

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

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

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Published: 08 July 2021

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GECCO '21

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SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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

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Deshmukh ASheth HPawar PTyagi A(2023)A Survey on Blockchain and Cryptocurrency-Based SystemsHandbook of Research on Blockchain Technology and the Digitalization of the Supply Chain10.4018/978-1-6684-7455-6.ch018(364-397)Online publication date: 21-Apr-2023
https://doi.org/10.4018/978-1-6684-7455-6.ch018
Alrumayh OAlmutairi A(2023)Novel secured distributed energy management structure for solar based smart grids incorporating minersSolar Energy10.1016/j.solener.2023.01.012251(134-145)Online publication date: Feb-2023
https://doi.org/10.1016/j.solener.2023.01.012
Alofi ABokhari MBahsoon RHendley R(2022)Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem FormulationIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31604917:4(910-922)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2022.3160491
Alofi ABahsoon RHendley R(2021)MinerRepu: A Reputation Model for Miners in Blockchain Networks2021 IEEE International Conference on Web Services (ICWS)10.1109/ICWS53863.2021.00100(724-733)Online publication date: Sep-2021
https://doi.org/10.1109/ICWS53863.2021.00100

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Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

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