Memetic algorithms, domain knowledge, and financial investing

How might domain knowledge constrain a genetic algorithm and systematically impact the algorithm’s traversal of the search space? In particular, in this paper the hypothesis is advanced that a semantic tree of financial knowledge can be used to influence the results of a genetic algorithm for financial investing problems. An algorithm is described, called the “Memetic Algorithm for Domain Knowledge”, and is instantiated in a software system. In mutation experiments, this system chooses financial ratios to use as inputs to a neural logic network which classifies stocks as likely to increase or decrease in value. The mutation is guided by a semantic tree of financial ratios. In crossover experiments, this system solves a portfolio optimization problem in which components of an individual represent weights on stocks; knowledge in the form of a semantic tree of industries determines the order in which components are sorted in individuals. Both synthetic data and real-world data are used. The experimental results show that knowledge can be used to reach higher fitness individuals more quickly. More interestingly, the results show how conceptual distance in the human knowledge can correspond to distance between evolutionary individuals and their fitness. In other words, knowledge might be dynamically used to at times increase the step size in a search algorithm or at times to decrease the step size. These results shed light on the role of knowledge in evolutionary computation and are part of the larger body of work to delineate how domain knowledge might usefully constrain the genetic algorithm.