Created by W.Langdon from gp-bibliography.bib Revision:1.7177
The vast majority of existing techniques aim to track dynamics of optima in very simple dynamic environments. But the research area in improving robustness in dynamic environments characterised by large, frequent and unpredictable changes is not yet widely explored. The three new algorithms were designed specifically to evolve robust solutions in these environments.
The first algorithm, behavioural diversity preservation, is a novel diversity preservation technique. The algorithm evolves more robust solutions by preserving population phenotypic diversity through the reduction of their behavioural inter-correlation and the promotion of individuals with unique behaviour.
The second algorithm, multiple-scenario training, is a novel population training and evaluation technique. The algorithm evolves more robust solutions by training a population simultaneously across a set of pre-constructed environment scenarios and by using a consistency-adjusted fitness measure to favour individuals performing well across the entire range of environment scenarios.
The third algorithm, committee voting is a novel final solution selection technique. The algorithm enhances robustness by breaking away from best-of-run tradition, creating a solution based on a majority-voting committee structure consisting of individuals evolved in a range of diverse environmental dynamics.
The thesis introduces a comprehensive real-world case application for the evaluation experiments. The case is a hedge fund stock selection application for a typical long-short market neutral equity strategy in the Malaysian stock market. The underlying technology of the stock selection system is GP which assists to select stocks by exploiting the underlying nonlinear relationship between diverse ranges of influencing factors. The three proposed algorithms are all applied to this case study during evaluation.
The results of experiments based on the case study demonstrate that all three new algorithms overwhelmingly outperform canonical GP in two aspects of the robustness criteria and conclude they are viable strategies for improving robustness of GP individuals when the environment of a task being optimised or learnt by a GP system is characterised by large, sudden, frequent and unpredictable changes.",
Genetic Programming entries for Wei Yan