A Comparison Between Memetic Algorithm and Seeded Genetic Algorithm for Multi-objective Independent Task Scheduling on Heterogeneous Machines

  • Héctor Joaquín Fraire Huacuja
  • Alejandro Santiago
  • Johnatan E. Pecero
  • Bernabé Dorronsoro
  • Pascal Bouvry
  • José Carlos Soto Monterrubio
  • Juan Javier Gonzalez Barbosa
  • Claudia Gómez Santillan
Part of the Studies in Computational Intelligence book series (SCI, volume 601)


This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II. A Wilcoxon rank-sum test shows significant differences in the diversity of solutions found but not in hypervolume. The memetic algorithm gets the best diversity for a bigger instance set from the state of the art.


Pareto Front Precedence Constraint Memetic Algorithm Linear Programming Model Optimal Makespan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



A. Santiago would like to thank CONACyT Mexico, for the support no. 360199.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Héctor Joaquín Fraire Huacuja
    • 1
  • Alejandro Santiago
    • 1
  • Johnatan E. Pecero
    • 2
  • Bernabé Dorronsoro
    • 2
  • Pascal Bouvry
    • 2
  • José Carlos Soto Monterrubio
    • 1
  • Juan Javier Gonzalez Barbosa
    • 1
  • Claudia Gómez Santillan
    • 1
  1. 1.Tecnológico Nacional de MexicoInstituto Tecnológico de Ciudad MaderoCiudad MaderoMexico
  2. 2.University of LuxembourgLuxembourgLuxembourg

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