A New Memory Based Variable-Length Encoding Genetic Algorithm for Multiobjective Optimization

  • Eduardo G. Carrano
  • Lívia A. Moreira
  • Ricardo H. C. Takahashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)


This paper presents a new memory-based variable-length encoding genetic algorithm for solving multiobjective optimization problems. The proposed method is a binary implementation of the NSGA2 and it uses a Hash Table for storing all the solutions visited during algorithm evolution. This data structure makes possible to avoid the re-visitation of solutions and it provides recovering and storage of data with low computational cost. The algorithm memory is used for building crossover, mutation and local search operators with a parameterless variable-length encoding. These operators control the neighborhood based on the density of points already visited on the region of the new solution to be evaluated. Two classical multiobjective problems are used to compare two variations of the proposed algorithm and two variations of the binary NSGA2. A statistical analysis of the results indicates that the memory-based adaptive neighborhood operators are able to provide significant improvement of the quality of the Pareto-set approximations.


Genetic Algorithm Local Search Hash Function Multiobjective Optimization Hash Table 
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 2011

Authors and Affiliations

  • Eduardo G. Carrano
    • 1
  • Lívia A. Moreira
    • 2
  • Ricardo H. C. Takahashi
    • 3
  1. 1.Department of Computer EngineeringCentro Federal de Educação Tecnológica de Minas GeraisBelo HorizonteBrazil
  2. 2.Department of Electrical EngineeringCentro Federal de Educação Tecnológica de Minas GeraisBelo HorizonteBrazil
  3. 3.Department of MathematicsUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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