A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening

Current methods to identify cut-off values for tumour-associated molecules (antigens) discrimination are based on statistics and brute force. These methods applied to cancer screening problems are very inefficient, especially with large data sets with many antigens being investigated. There is a long wait to produce outcomes for clinicians, high performance computing is required, the best solution is not likely to be achieved and scalability is an issue. Cancer research is therefore limited in the number of antigens the methods can efficiently handle, and good solutions are potentially missed. We present an alternative evolutionary method based on Genetic Algorithms and Harmony Search to accelerate clinical research and to enable the consideration of a larger number of candidate antigens during the designing of the screening. We show that compared to the traditional methodology employed by clinicians, our approach is able to produce better results in a timely manner.