We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Genetic programming makes it possible to automatically search the space of possible programs. First we evolved a behavior-based control architecture using computer simulations. Th...
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...
Investors vary with respect to their expected return and aversion to associated risk, and hence also vary in their performance expectations of the stock market portfolios they hol...
In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to...
Eldan Goldenberg, Jacob R. Garcowski, Randall D. B...