A tree-based GA representation for the portfolio optimization problem

Recently, a number of works have been done on how to use Genetic Algorithms to solve the Portfolio Optimization problem, which is an instance of the Resource Allocation problem class. Almost all these works use a similar genomic representation of the portfolio: An array, either real, where each element represents the weight of an asset in the portfolio, or binary, where each element represents the presence or absence of an asset in the portfolio. In this work, we explore a novel representation for this problem. We use a tree structure to represent a portfolio for the Genetic Algorithm. Intermediate nodes represent the weights, and the leaves represent the assets. We argue that while the Array representation has no internal structure, the Tree approach allows for the preservation of building blocks, and accelerates the evolution of a good solution. The initial experimental results support our opinions regarding this new genome representation. We believe that this approach can be used for other instances of Resource Allocation problems.