Diversity Maintenance Mechanism for Multi-Objective Genetic Algorithms Using Clustering and Network Inversion

  • Tomoyuki Hiroyasu
  • Kenji Kobayashi
  • Masashi Nishioka
  • Mitsunori Miki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


One of the major issues in applying multi-objective genetic algorithms to real-world problems is how to reduce the large number of evaluations. The simplest approach is a search with a small population size. However, the diversity of solutions is often lost with such a search. To overcome this difficulty, this paper proposes a diversity maintenance mechanism using clustering and Network Inversion that is capable of preserving diversity by relocating solutions. In addition, the proposed mechanism adopts clustering of training data sets to improve the accuracy of relocation. The results of numerical experiments on test functions and diesel engine emission and fuel economy problems showed that the proposed mechanism provided solutions with high diversity even when the search was performed with a small number of solutions.


Diesel Engine Multiobjective Optimization Inverse Analysis Neighbor Relationship Target Solution 
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

  • Tomoyuki Hiroyasu
    • 1
  • Kenji Kobayashi
    • 2
  • Masashi Nishioka
    • 2
  • Mitsunori Miki
    • 3
  1. 1.Faculty of Life and Medical SciencesDoshisha UniversityKyotoJapan
  2. 2.Graduate School of EngineeringDoshisha UniversityKyotoJapan
  3. 3.Faculty of Science and EngineeringDoshisha UniversityKyotoJapan

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