Advertisement

Hybrid Model of Genetic Algorithm and Cultural Algorithms for Optimization Problem

  • Fang Gao
  • Hongwei Liu
  • Qiang Zhao
  • Gang Cui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

To solve constrained optimization problems, we propose to integrate genetic algorithm (GA) and cultural algorithms (CA) to develop a hybrid model (HMGCA). In this model, GA’s selection and crossover operations are used in CA’s population space. A direct comparison-proportional method is employed in GA’s selections to keep a certain proportion of infeasible but better (with higher fitness) individuals, which is beneficial to the optimization. Elitist preservation strategy is also used to enhance the global convergence. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semi-feasible and feasible region of the optimization problem to improve search direction in GA. Thus it is possible to enhance search ability and to reduce computational cost. A simulation example shows the effectiveness of the proposed approach.

Keywords

Genetic Algorithm Hybrid Model Constrain Optimization Problem Real Code Genetic Algorithm Belief 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reynolds, R.G.: An Introduction to Cultural Algorithms. In: Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 108–121. World Scientific Publishing, Singapore (1994)Google Scholar
  2. 2.
    Reynolds, R.G., Chung, C.J.: A Self-adaptive Approach to Representation Shifts in Cultural Algorithms, vol. 3, pp. 94–99. IEEE, Los Alamitos (1996)Google Scholar
  3. 3.
    Becerra, R.L., Coello Coello, C.A.: Culturizing Differential Evolution for Constrained Optimization. In: Proceedings of the Fifth Mexican International Conference in Computer Science, pp. 304–311. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  4. 4.
    Jin, X.D., Reynolds, R.G.: Mining Knowledge in Large Scale Databases Using Cultural Algorithms with Constraint Handling Mechanisms. In: Proceeding of the 2000 congress on evolutionary computation, pp. 1498–1506. IEEE, Los Alamitos (2000)Google Scholar
  5. 5.
    Ho, N.B., Tay, J.C.: GENACE: An Efficient Cultural Algorithm for Solving the Flexible Job-Shop Problem. In: Proceeding of 2004 Congress on Evolutionary Computation, vol. 2, pp. 1759–1766 (2004)Google Scholar
  6. 6.
    Yuan, X.H., Yuan, Y.B.: Application of Cultural Algorithm to Generation Scheduling of Hydrothermal Systems. Energy Conversion and Management 47, 2192–2201 (2006)CrossRefGoogle Scholar
  7. 7.
    Lin, F., Shieh, H., Shyu, K., Huang, P.: Online Gain-tuning IP Controller Using Real-Coded Genetic Algorithm. Electric Power Systems Research 72, 157–169 (2004)CrossRefGoogle Scholar
  8. 8.
    Arfiadi, Y., Hadi, M.N.S.: Optimal Direct (static) Output Feedback Controller Using Real Coded Genetic Algorithms. Computers and Structures 79, 1625–1634 (2001)CrossRefGoogle Scholar
  9. 9.
    Ha, J., Fung, R., Han, C.: Optimization of an Impact Drive Mechanism Based on Real-coded Genetic Algorithm. Sensors and Actuators 121, 488–493 (2005)CrossRefGoogle Scholar
  10. 10.
    Yan, S.Z., Zheng, K., Zhao, Q., Zhang, L.: Optimal Placement of Active Members for Truss Structure Using Genetic Algorithm. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3645, pp. 386–395. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Blanco, A., Delgado, M., Pegalajar, M.C.: A Real Coded Genetic Algorithm for Training Recurrent Neural Networks. Neural Networks 14, 93–105 (2001)CrossRefGoogle Scholar
  12. 12.
    Myung, H., Kim, J.H.: Hybrid Evolutionary Programming for Heavily Constrained Problems. BioSystems 38, 29–43 (1996)CrossRefGoogle Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)MATHGoogle Scholar
  14. 14.
    Li, M.Q., Kou, J.Z., Lin, D., Li, S.Q.: Based theory and Application of Genetic Algorithm, 3rd edn. Science Press (2004)Google Scholar
  15. 15.
    Jin, X.D., Reynolds, R.G.: Using Knowledge-Based Evolutionary Computation to Solve Nonlinear Constraint Optimization Problems: a Cultural Algorithm Approach, pp. 1672–1678. IEEE, Los Alamitos (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fang Gao
    • 1
  • Hongwei Liu
    • 1
  • Qiang Zhao
    • 2
  • Gang Cui
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Traffic Transportation EngineeringNortheast Forestry UniversityHarbinP.R. China

Personalised recommendations