Design Issues in a Multiobjective Cellular Genetic Algorithm

  • Antonio J. Nebro
  • Juan J. Durillo
  • Francisco Luna
  • Bernabé Dorronsoro
  • Enrique Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)


In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm following the canonical cGA model, i.e., each individual interacts with those ones belonging to its neighborhood, so that a new individual is obtained using the typical selection, crossover, and mutation operators within this neighborhood. An external archive is used to store the non-dominated solutions found during the evolution process. With this basic model in mind, there are many different design issues that can be faced. Among them, we focus here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two different replacement strategies. We evaluate the resulting algorithms using a benchmark of problems and compare the best of them against two state-of-the-art genetic algorithms for multiobjective optimization. The obtained results indicate that the cGA model is a promising approach to solve this kind of problem.


Pareto Front Multiobjective Optimization Generational Distance Design Issue 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 Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio J. Nebro
    • 1
  • Juan J. Durillo
    • 1
  • Francisco Luna
    • 1
  • Bernabé Dorronsoro
    • 1
  • Enrique Alba
    • 1
  1. 1.Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga (Spain) 

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