Advertisement

Multi-objective Optimal Public Investment: An Extended Model and Genetic Algorithm-Based Case Study

  • Lei Tian
  • Liyan Han
  • Hai Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

Under the multi-region and multi-sector consideration, the previous double-objective optimal public investment model is extended to involve optimal employment rate objective and time-flow total income maximization objective first. Then genetic algorithm is applied to solve the multi-objective model. Finally a case study is carried out to verify the superiority of the genetic algorithm-based solution to traditional public investment distribution approach.

Keywords

Genetic Algorithm Employment Rate Public Investment Total Income Binary String 
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.
    Chen, B.L.: Economic growth with an optimal public spending composition. Oxford Economic Papers 58(1), 123–136 (2006)CrossRefGoogle Scholar
  2. 2.
    Yamano, N., Ohkawara, T.: The Regional Allocation of Public Investment: Efficiency or Equity. Journal of Regional Science 40(2), 205 (2000)CrossRefGoogle Scholar
  3. 3.
    Smith, G.: Creating the conditions for public investment to deliver full employment and environmental sustainability. International Journal of Environment, Workplace and Employment 1(3/4), 258–264 (2006)CrossRefGoogle Scholar
  4. 4.
    Tian, L., Liu, L., Han, L., Huang, H.: A Genetic Algorithm-Based Double-Objective Multi-constraint Optimal Cross-Region Cross-Sector Public Investment Model. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4222, pp. 470–479. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Sakashita, N.: Regional Allocation of Public Investment. Paper of the Regional Science Association, vol.19, pp. 161–162 (1967)Google Scholar
  6. 6.
    Yang, X.L.: Improving Portfolio Efficiency: A Genetic Algorithm Approach. Computational Economics 28(1), 1–14 (2006)zbMATHCrossRefGoogle Scholar
  7. 7.
    Gen, M., Cheng, R.W.: Genetic Algorithms and Engineering Optimization. John Wiley & Sons, Chichester (2000)Google Scholar
  8. 8.
    Wróblewski, J.: Finding Minimal Reducts Using Genetic Algorithm (extended version). In: Wang, P.P. (ed.) JCIS’95, pp. 186–189 (1995)Google Scholar
  9. 9.
    Hsieh, T.Y., Liu, H.L.: Genetic Algorithm for Optimization of Infrastructure Investment under Time-Resource Constraints. Computer-Aided Civil and Infrastructure Engineering 19(3), 203–212 (2004)CrossRefGoogle Scholar
  10. 10.
    Metenidis, M.F., Witczak, M., Korbicz, J.: A Novel Genetic Programming Approach to Nonlinear System Modeling: Application to the DAMADICS Benchmark Problem. Engineering Applications of Artificial Intelligence 17(4), 363–370 (2004)CrossRefGoogle Scholar
  11. 11.
    Pan, Y., Yu, Z.W., Liu, K.J., Dou, W.: A New Multi-Objective Programming Model of QoS-based Multicast Routing Problem. Computer Engineering and Application 19, 155–157 (2003)Google Scholar
  12. 12.
    Guo, H.Y., Zhang, L., Jiang, J.: Two-Stage Structural Damage Detection Method with Genetic Algorithms. Journal of Xi’an Jiaotong University 39(5), 485–489 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Lei Tian
    • 1
  • Liyan Han
    • 1
  • Hai Huang
    • 2
  1. 1.School of Economics and Management, Beihang University, Beijing 100083P.R. China
  2. 2.School of Computer Science and Engineering, Beihang University, Beijing 100083P.R. China

Personalised recommendations