Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization. (June 2017)

• Record Type: Journal Article
• Authors: Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting
• Other Names: Ho Dean guest-editor.; Zarrinpar Ali guest-editor.
• Is Part Of: SLAS technology. Volume 22:Number 3(2017)
• Page Start: 289
• Page End: 305
• Publication Date: 2017-06
• Subjects: differential evolution -- self-adaptation -- population size -- global optimization -- artificial intelligence; Medical laboratory technology -- Periodicals; Laboratories -- Equipment and supplies -- Periodicals; Diagnosis, Laboratory -- Periodicals; 616.075
• DOI: 10.1177/2472630317690318
• Languages: English
• ISSNs: 2472-6303

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.