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Memetic Algorithm for Solving the Problem of Social Portfolio Using Outranking Model

  • Claudia G. Gómez S.
  • Eduardo R. Fernández Gonzalez
  • Laura Cruz Reyes
  • S. Samantha Bastiani M.
  • Gilberto Rivera Z.
  • Victoria Ruız M.
Part of the Studies in Computational Intelligence book series (SCI, volume 451)

Abstract

The government institutions at all levels, foundations with private funds or private companies that support social projects receiving public funds or budget to develop its own social projects often have to select the projects to support and allocate budget to each project. The choice is difficult when the available budget is insufficient to fund all projects or proposals whose budget requests have been received, together with the above it is expected that approved projects have a significant social impact. This problem is known as the portfolio selection problem of social projects. An important factor involved in the decision to make the best portfolio, is that the objectives set out projects that are generally intangible, such as the social, scientific and human resources training. Taking into account the above factors in this paper examines the use of multi objective methods leading to a ranking of quality of all selected projects and allocates resources according to priority ranking projects until the budget is exhausted. To verify the feasibility of ranking method for the solution of problem social portfolio constructed a population memetic evolutionary algorithm, which uses local search strategies and cross adapted to the characteristic of the problem. The experimental results show that the proposed algorithm has a competitive performance compared to similar algorithms reported in the literature and on the outranking model is a feasible option to recommend a portfolio optimum, when little information and the number of projects is between 20 and 70.

Keywords

Pareto Front Multiobjective Optimization Optimal Portfolio Memetic Algorithm Portfolio Selection Problem 
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 2013

Authors and Affiliations

  • Claudia G. Gómez S.
    • 1
  • Eduardo R. Fernández Gonzalez
    • 2
  • Laura Cruz Reyes
    • 1
  • S. Samantha Bastiani M.
    • 1
  • Gilberto Rivera Z.
    • 1
  • Victoria Ruız M.
    • 1
  1. 1.Instituto Tecnológico de Ciudad MaderoTamaulipasMéxico
  2. 2.Universidad Autónoma de Sinaloa (UAS)Los MochisMéxico

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