Abstract
A memory-enabled program representation in strongly-typed Genetic Programming (GP) is compared against the standard representation in a number of financial time-series modelling tasks. The paper first presents a survey ofGP systems that utilisememory. Thereafter, a number of simulations show thatmemory-enabled programs generalise better than their standard counterparts in most datasets of this problem domain.
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CITATION STYLE
Agapitos, A., Brabazon, A., & O’Neill, M. (2016). Genetic programming with memory for financial trading. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9597, pp. 19–34). Springer Verlag. https://doi.org/10.1007/978-3-319-31204-0_2
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