Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA

  • Naoki Hamada
  • Jun Sakuma
  • Shigenobu Kobayashi
  • Isao Ono
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


This paper presents a Genetic Algorithm (GA) for multi-objective function optimization. In multi-objective function optimization, we believe that GA should adaptively switch search strategies in the early stage and the last stage for effective search. Non-biased sampling and family-wise alternation are suitable to overcome local Pareto optima in the early stage of search, and extrapolation-directed sampling and population-wise alternation are effective to cover the Pareto front in the last stage. These situation-dependent requests make it difficult to keep good performance through the whole search process by repeating a single strategy. We propose a new GA that switches two search strategies, each of which is specialized for global and local search, respectively. This is done by utilizing the ratio of non-dominated solutions in the population. We examine the effectiveness of the proposed method using benchmarks and a real-world problem.


Local Search Pareto Front Pareto Optimal Solution Global Search Objective Space 
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 2008

Authors and Affiliations

  • Naoki Hamada
    • 1
  • Jun Sakuma
    • 1
  • Shigenobu Kobayashi
    • 1
  • Isao Ono
    • 1
  1. 1.Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan

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