TY - JFULL AU - Hassan Zarei and Ali Vahidian Kamyad and Sohrab Effati PY - 2011/8/ TI - An Adaptive Memetic Algorithm With Dynamic Population Management for Designing HIV Multidrug Therapies T2 - International Journal of Medical and Health Sciences SP - 254 EP - 262 VL - 5 SN - 1307-6892 UR - https://publications.waset.org/pdf/4022 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 55, 2011 N2 - In this paper, a mathematical model of human immunodeficiency virus (HIV) is utilized and an optimization problem is proposed, with the final goal of implementing an optimal 900-day structured treatment interruption (STI) protocol. Two type of commonly used drugs in highly active antiretroviral therapy (HAART), reverse transcriptase inhibitors (RTI) and protease inhibitors (PI), are considered. In order to solving the proposed optimization problem an adaptive memetic algorithm with population management (AMAPM) is proposed. The AMAPM uses a distance measure to control the diversity of population in genotype space and thus preventing the stagnation and premature convergence. Moreover, the AMAPM uses diversity parameter in phenotype space to dynamically set the population size and the number of crossovers during the search process. Three crossover operators diversify the population, simultaneously. The progresses of crossover operators are utilized to set the number of each crossover per generation. In order to escaping the local optima and introducing the new search directions toward the global optima, two local searchers assist the evolutionary process. In contrast to traditional memetic algorithms, the activation of these local searchers is not random and depends on both the diversity parameters in genotype space and phenotype space. The capability of AMAPM in finding optimal solutions compared with three popular metaheurestics is introduced. ER -