A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening

Figueredo, Grazziela P.; Shi, Peng; Parkes, Andrew J.; Evans, Keith; Garibaldi, Jonathan M.; Negm, Ola; Tighe, Patrick J.; Sewell, Herbert F.; Robertson, John

Abstract

Current methods to identify cutoff 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.

Citation

Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., …Robertson, J. (2019). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. In 2019 IEEE Congress on Evolutionary Computation (CEC) (95-102). https://doi.org/10.1109/CEC.2019.8790316

• Conference Name: 2019 IEEE Congress on Evolutionary Computation (CEC)
• Conference Location: Wellington, New Zealand
• Start Date: Jun 10, 2019
• End Date: Jun 13, 2019
• Acceptance Date: Mar 8, 2019
• Online Publication Date: Aug 8, 2019
• Publication Date: 2019-06
• Deposit Date: Apr 16, 2019
• Publicly Available Date: Apr 26, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 95-102
• Book Title: 2019 IEEE Congress on Evolutionary Computation (CEC)
• ISBN: 978-1-7281-2154-3
• DOI: https://doi.org/10.1109/CEC.2019.8790316
• Keywords: Genetic algorithms, Multiple objectives, Composite chromosome, Monte Carlo, Harmony search, Cancer-screening, Colorectal cancer, Breast cancer, Lung cancer
• Public URL: https://nottingham-repository.worktribe.com/output/1659618
• Publisher URL: https://ieeexplore.ieee.org/document/8790316
• Related Public URLs: http://cec2019.org/
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”