An Evolutionary Constraint-Handling Technique for Parametric Optimization of a Cancer Immunotherapy Model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8673711
Pages (from-to)151-162
Journal / PublicationIEEE Transactions on Emerging Topics in Computational Intelligence
Volume3
Issue number2
Online published25 Mar 2019
Publication statusPublished - Apr 2019

Abstract

Recent studies have shown that evolutionary constraint-handling techniques are capable of solving optimization problems with constraints. However, these techniques are often evaluated based on benchmark test functions instead of real-world problems. This paper presents an application of evolutionary constrained parametric optimization for a breast cancer immunotherapy model formulated based on biological principles and limited clinical results. It proposes a new constraint-handling technique that partitions the population into different sections to enhance the evolutionary search diversity. In addition, the upper bound of each section is reduced dynamically to drive the convergence of individuals toward the feasible solution region. Experimental results show the effectiveness and robustness of the proposed constraint-handling approach in solving parametric optimization problems. Moreover, the evolutionary optimized cancer immunotherapy model can be used for prognostic outcomes in clinical trials and the predictability is considered significant for such a parametric optimization approach.

Research Area(s)

  • Constraint-handling techniques, parametric optimization problems, ε-SEC, data-driven optimization

Citation Format(s)

An Evolutionary Constraint-Handling Technique for Parametric Optimization of a Cancer Immunotherapy Model. / Xu, Weinan; Xu, Jian-Xin; He, Danhua; Tan, Kay Chen.

In: IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 3, No. 2, 8673711, 04.2019, p. 151-162.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review