Vaginal microbicides for the prevention of HIV transmission

The human immunodeficiency virus (HIV) kills more people worldwide than any other infectious disease. Approximately 42 million people, mostly in Africa and Asia, are currently infected with HIV (Figure 3.1), and 5 million new infections occur every year (AIDS Epidemic Update, 2002). It is estimated that 22 milIion people have died since the first clinical evidence of AIDS (acquired immunodeficiency syndrome) emerged in 1981 ('Mobilization for Microbicides' ~ The Rockfeller Foundation). HIV is generally transmitted in one of three ways: through unprotected sexual intercourse, blood-to-blood contact, and mother-to-child transmission. Once the virus has entered the body, it invades the cells of the immune system and initiates the production of new virus particles with concomitant destruction of the immune cells. As the number of immune cells in the body slowly declines, weight loss, debilitation, and eventually death occur due to opportunistic infections or cancers. Although AIDS is presently incurable, highly active antiretroviral therapy (HAART), where a cocktail of potent antiretroviral drugs are administered daily to HIV-positive patients to control the viral load, has resulted in dramatic reductions in HIV-related morbidity and mortality in the developed world

A hybrid genetic algorithm for the multi-depot vehicle routing problem

The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction because more customers can be served in a shorter time. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots, or multi-depot VRP (MDVRP). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, two hybrid genetic algorithms (HGAs) are developed in this paper. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method and the nearest neighbor heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different problem sizes. It is proved that the performance of HGA2 is superior to that of HGA1 in terms of the total delivery time.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem. © 2013 Published by Elsevier Ltd.