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Prediction of energy consumption in a NSGA-II-based evolutionary algorithm

Published: 06 July 2018 Publication History

Abstract

A deeper understanding in how the power consumption of evolutionary algorithms behaves is necessary to keep meeting high quality results without wasting energy resources. This paper presents a black-box model for predicting the energy consumption of the NSGA-II-based Parallel Islands approach to Multiobjective Feature Selection (pi-MOFS). We analyzed the power usage of each stage in pi-MOFS when applied to a brain-computer interface classification task. Fitness evaluation showed as the most relevant stage for the case study presented in time and power consumption. The results showed a 98.81% prediction accuracy for the eight experiments designed. We believe that our findings and methodology can be used to apply pi-MOFS, NSGA-II and other EAs to current optimization problems from an energy-aware perspective.

References

[1]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197. http://ieeexplore.ieee.org/abstract/document/996017/
[2]
Julio Ortega, Dragi Kimovski, John Q. Gan, Andrés Ortiz, and Miguel Damas. 2017. A Parallel Island Approach to Multiobjective Feature Selection for Brain-Computer Interfaces. In International Work-Conference on Artificial Neural Networks (IWANN): Advances in Computational Intelligence, Vol. 10305. Springer International Publishing, Cham, 16--27.
[3]
F. Fernández de Vega, F. Chávez, J. Díaz, J. A. García, P. A. Castillo, Juan J. Merelo, and C. Cotta. 2016. A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms. In Parallel Problem Solving from Nature - PPSN XIV (Lecture Notes in Computer Science). Springer, Cham, 548-557.

Cited By

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  • (2024)Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-IIIEAI Endorsed Transactions on Energy Web10.4108/ew.572111Online publication date: 10-Apr-2024
  • (2024)Stepping into Industry 4.0-based optimization model: a hybrid of the NSGA-III and MOAOAKybernetes10.1108/K-08-2023-1580Online publication date: 14-Jun-2024

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  1. Prediction of energy consumption in a NSGA-II-based evolutionary algorithm

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      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2018
      1968 pages
      ISBN:9781450357647
      DOI:10.1145/3205651
      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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      Publication History

      Published: 06 July 2018

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      Author Tags

      1. NSGA-II
      2. black-box model
      3. energy-aware computing
      4. evolutionary algorithm
      5. fitness evaluation
      6. parallel computing

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      • FEDER funds
      • Ministry of Economy and Competitiveness

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

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      View all
      • (2024)Development of an Energy Planning Model Using Temporal Production Simulation and Enhanced NSGA-IIIEAI Endorsed Transactions on Energy Web10.4108/ew.572111Online publication date: 10-Apr-2024
      • (2024)Stepping into Industry 4.0-based optimization model: a hybrid of the NSGA-III and MOAOAKybernetes10.1108/K-08-2023-1580Online publication date: 14-Jun-2024

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