On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II

  • Antonio J. Nebro
  • Juan J. Durillo
Part of the Studies in Computational Intelligence book series (SCI, volume 193)


Genetic Algorithms (GAs) are among the most popular techniques to solve multi-objective optimization problems, with NSGA-II being the most well-known algorithm in the field. Although most of multi-objective GAs (MOGAs) use a generational scheme, in the last few years some proposals using a steady-state scheme have been developed. However, studies about the influence of using those selection strategies in MOGAs are scarce. In this chapter we implement a steady-state version of NSGA-II, which is a generational MOGA, and we compare the two versions with a set of four state-of-the-art multi-objective metaheuristics (SPEA2, OMOPSO, AbYSS, and MOCell) attending to two criteria: the quality of the resulting approximation sets to the Pareto front and the convergence speed of the algorithms. The obtained results show that search capabilities of the steady-state version of NSGA-II significantly improves the original version, providing very competitive results in terms of the quality of the obtained Pareto front approximations and the convergence speed.


Pareto Front Convergence Speed Optimal Pareto Front Multiobjective Optimization Problem Multiobjective Evolutionary Algorithm 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antonio J. Nebro
    • 1
  • Juan J. Durillo
    • 1
  1. 1.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaUniversity of MálagaMálagaSpain

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