A Novel Dynamic Multi-objective Evolutionary Algorithm with an Adaptable Roulette for the Selection of Operators

  • Héctor Joaquín Fraire HuacujaEmail author
  • Eduardo Rodríguez del Angel
  • Juan Javier González Barbosa
  • Alejandro Estrada Padilla
  • Lucila Morales Rodríguez
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


In this chapter, the optimization of Dynamic Multi-Objective Problems (DMOP) is approached. To solve this kind of problems several evolutionary algorithms with a static selection of operators are reported in the literature. In this work, a new evolutionary algorithm with that an online operator selector is proposed. The operator choice is guided by a self-adapting roulette that modifies the probabilities of usage for each operator. The evolutionary algorithm proposed follows the classical generational scheme of an evolutionary algorithm, but each offspring is constructed by selecting an operator from an operator’s pool based on a probability regulated by the roulette. A series of experiments were done to assess the performance of the proposed algorithm that includes a set of state-of-the-art algorithms, a set of standard instances and statistical hypothesis tests to support the conclusions.


Dynamic multi-objective optimization Evolutionary algorithm Adaptive algorithm 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Héctor Joaquín Fraire Huacuja
    • 1
    Email author
  • Eduardo Rodríguez del Angel
    • 1
  • Juan Javier González Barbosa
    • 1
  • Alejandro Estrada Padilla
    • 1
  • Lucila Morales Rodríguez
    • 1
  1. 1.Tecnológico Nacional de MéxicoInstituto Tecnológico de Ciudad MaderoMexico

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