Memetic Portfolio Optimization
While researching multi-objective optimization for large search spaces and clustering, Quantivity stumbled upon some interesting recent work by Aranha and Iba applying memetic and biologic algorithms. Latter work appears along the line of Brabazon and O’Neill Biologically Inspired Algorithms for Financial Modelling (which, responding to previous comments, may be potentially useful for those looking for published examples of GA applied to trading, as it includes numerous simplistic case studies in Part III).
First, applying memetic algorithms for portfolio optimization and trading rule evolution. Memetic seems potentially interesting as it combines local search with genetic evolution, and thus may have some interesting uses where classic GA has proven unsuccessful:
Genetic algorithm improves the solution in large strokes, while the local optimization fine tunes the solutions generated by the GA.
- Aranha and Iba, Using Memetic Algorithms to Improve Portfolio Performance in Static and Dynamic Trading Scenarios
- Aranha and Iba, Memetic Tree-based Genetic Algorithm and its application to Portfolio Optimization
- Aranha and Iba, Optimization of the Trading Rule in Foreign Exchange using Genetic Algorithms
Second, applying biologic (ant) algos for complex shape clustering:
Shown that a form of clustering happens from this low level rules. Ant clustering has its positive and negative points when compared to more traditional techniques, such as k-means, but it can be very useful for the clustering of data that is shaped into complex shapes.
Having never used ant clustering in practice, a healthy dose of skepticism seems warranted (if only due to its name).
Full list of Aranha publications includes earlier work on more classic GAs.