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 experts. We describe the multi-stage training, testing and validation process that we have integrated with GP...

We describe a design process, Emergent Design, that draws upon techniques and approaches from the disciplines of computer science and artificial intelligence in addition to architecture. The process focuses on morphology, emphasizing the emergent and adaptive properties of architectural form and...

In this paper we address the problem of program discovery as defined by Genetic Programming. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have...

In this paper we rigorously formulate the Schema Theorem for Genetic Programming (GP). This involves defining a schema, schema order, and defining length and accounting for the variable length and the non-homologous nature of GP'S representation. The GP Schema Theorem and the related notion of a...

This paper emphasizes the general value of a hierarchical variable length representation for program induction by demonstrating that different search strategies and operators complementary to them can be used to obtain solutions. It presents a comparison of Genetic Programming (GP) with...

This paper presents building block functions, i.e., functions in which explicit schemas of high fitness are defined (BB functions, for short) which are useful in investigating the character of Genetic Programming (GP) search. One conjecture we believe to be answerable through experimentation...