title: Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.
abstract: Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.
authors.affiliation: 1 School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.|2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
authors.forename: Ieong
authors.initials: I
authors.lastname: Wong
authors.affiliation: 2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
authors.forename: Wenjia
authors.initials: W
authors.lastname: Liu
authors.affiliation: 1 School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, China.|2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
authors.forename: Chih-Ming
authors.initials: CM
authors.lastname: Ho
authors.affiliation: 2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
authors.forename: Xianting
authors.initials: X
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D004359:Drug Therapy, Combination / Q000379:methods*; D006801:Humans
keywords: artificial intelligence; differential evolution; global optimization; population size; self-adaptation