The Genetic Algorithm: An Application on Portfolio Optimization

The Genetic Algorithm: An Application on Portfolio Optimization

Burcu Adıguzel Mercangöz (Istanbul University, Turkey) and Ergun Eroglu (Istanbul University, Turkey)
DOI: 10.4018/978-1-7998-8048-6.ch040
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The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.
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Evolutionary Algorithms

Evolutionary Algorithms (EAs) are the metaheuristics algorithms that are based on population (Vejandla, 2009). The algorithms use processes from the biological evolution such as selection, mutation and recombination (Slim et al. 2011). These algorithms commonly use the natural evolution concept. There are several heuristics classified as Evolutionary Algorithms (Streichert, 2002). The common characteristics of EA can be listed as;

  • 1.

    Easy to implement,

  • 2.


  • 3.


  • 4.

    Allows wide variety of extensions and constraints different from traditional methods,

  • 5.

    Easily combined with other optimization techniques,

  • 6.

    Can be extended to Multi-objective optimization.

The most important Evolutionary Algorithms can be listed as Genetic Algorithms, Genetic Programming, Evolutionary Strategies, Evolutionary Programming and Learning Classifier Systems (Streichert, 2002).

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