Optimizing Technical Trading Strategies with Split Search Genetic Algorithms

Genetic algorithms can be used to search a space and find a near optimal solution to a problem. Standard genetic algorithms are composed of three operators: reproduction, crossover and mutation. Mutation is the occasional random alteration of the value of a bit-string. The role of the mutation operator is to introduce some randomness into the search. Many researchers erroneously consider mutation unimportant when compared to reproduction and crossover. They argue that the mutation rate is so low that it may as well be non-existent. However, mutation can prevent the search from ending in a local optima. A variant of the standard genetic algorithm, that splits the population into two and applies a high mutation rate to one of the sub-populations, is proposed and tested in this study. The idea is that the high mutation rate will permit the sub-population to ‘jump’ out of a local minima. It is found that the approach optimizes well on a test-bed of functions. The same approach is then applied to a practical optimization problem in finance.