Artificial evolution approaches to address the data challenges

non-peer-reviewed
This thesis is concerned with leveraging the power of evolutionary computation to find structure or models within complex datasets. We opt for a data-centric approach to this issue, paying particular (but not exclusive) attention to real world datasets from the financial forecasting domain. Financial forecasting is an alluring endeavour that does not enjoy the sort of widely acknowledged success as do other popular areas of EC application such as circuit design, scheduling or industrial process control. Data believed to be pertinent to financial forecasting tends to be large and multi-dimensional, it is extremely non-linear and abundant with such a degree of noise that it has led to the popular school of thought that forecasting financial datasets is fundamentally impossible. Furthermore, there exists no widely accepted consensus, theoretical or otherwise, on the dynamics of financial markets. These and other troubling attributes make financial forecasting and the interpretation of financial data a fruitful testbed for evolutionary algorithms. By addressing the unique data challenges inspired by this domain we advance the understanding and capabilities of evolutionary computation. In particular our investigations result in the creation and proof of concept of two brand new evolutionary algorithms; the Hybrid Forecasting System -- used to evolve practical objectives to a problem, and, Evolutionary Multidimensional Scaling -- a new Multidimensional Scaling algorithm more appropriate to financial applications than existing MDS algorithms. In addition to these new algorithms this thesis demonstrates the application of EC to a new source of data (quantitative news sentiment), highlights a phenominon detrimental to EA's in noisy environments and demonstrates three new styles of data visualization.