Evolutionary approaches to computational economics: Application to portfolio optimization

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter examines the use of genetic algorithms (GA) to perform the task of continuously rebalancing a portfolio, targeting specific risk and return characteristics. The portfolio is comprised of a number of arbitrarily performing trading strategies, plus a risk-free strategy in order to rebalance in a way similar to the traditional Capital Asset Pricing Model (CAPM) method of rebalancing portfolios. A format is presented for the design of a fitness function appropriate to the task, which is evaluated by examining the final results. The results of targeting both risk and return were investigated and compared, as well as optimizing the non-targeted variable to create efficient portfolios. The findings showed that GA is, indeed, a viable tool for optimizing a targeted portfolio using the presented fitness function.

Original languageEnglish
Title of host publicationAdvanced Information and Knowledge Processing
PublisherSpringer London
Number of pages17
Publication statusPublished - 2013

Publication series

NameAdvanced Information and Knowledge Processing
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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