An Evolutionary Weight Encoding Scheme and Crossover Methodology in Portfolio Assets Allocation

Abstract

Most of GA-based portfolio assets allocation uses normalization method to allocate investment asset's weight. However, the normalization process will cause unease converging and even diverging characteristics, because it changes the gene's relativity of address in chromosome. In this paper, we propose a weighed encoding scheme and crossover algorithm to allocate suitable assets in portfolio. Each gene encoded as a real number in a chromosome is denoted as the weighted number of assets in our approach. Due to no specific relationship assumed in our encoding scheme, the crossover process would not influence overall converging speed. In addition, in order to avoid losing optimal asset allocations through quicker converging, we also introduce the expanding rate to allow enlarging the possible range of asset allocation weight during the evolutionary process. Our experiments also show that higher expanding rate produces higher excess profit of investment.