The Application of Evolutionary Computation to Financial Time Series Prediction

A genetic algorithm (GA) is a search technique that falls within the inter-section of optimization algorithms and evolutionary algorithms. GAs became a widely recognized optimization method as a result of the work of John Holland in the early 1970s (Holland 1975). Genetic programming (GP) is an evolutionary algorithm that optimizes a population of computer programs according to a fitness landscape determined by a program’s ability to perform a user-defined task. The first experiments with GP were reported by Smith (1980) and Cramer (1985), and the seminal book is Koza (1992). GP generalizes GAs, but Woodward (2003) argues that the two are effec-tively equivalent and it is the representation that is important. On average, GAs/GP are no better or worse than any other search/optimization algorithm (Wolpert and Macready 1997). Neely, Weller and Dittmar (1997) use genetic programming techniques to find technical trading rules and find strong evidence of economically significan