Mobile applications are being used in a great range of fields and application areas. As a result, many research fields have focused on the study and improvement of such devices. The current Smartphones are the best ex...Mobile applications are being used in a great range of fields and application areas. As a result, many research fields have focused on the study and improvement of such devices. The current Smartphones are the best example of the research and the evolution of these technologies. Moreover, the software design and development is progressively more focused on the user; finding and developing new mobile interaction models. In order to do so, knowing what kind of problems the users could have is vital to enhance a bad interaction design. Unfortunately, a good software quality evaluation takes more time than the companies can invest. The contribution revealed in this work is a new approach to quality testing methodology focused on mobile interactions and their context in use where external capturing tools, such as cameras, are suppressed and the evaluation environments are the same as the user will use the application. By this approach, the interactions can be captured without changing the context and consequently, the data will be more accurate, enabling the evaluation of the quality-in-use in real environments.展开更多
We propose a multi-crossover and adaptive island based population algorithm(MAIPA).This technique divides the entire population into subpopulations,or demes,each with a different crossover function,which can be switch...We propose a multi-crossover and adaptive island based population algorithm(MAIPA).This technique divides the entire population into subpopulations,or demes,each with a different crossover function,which can be switched according to the efficiency.In addition,MAIPA reverses the philosophy of conventional genetic algorithms.It gives priority to the autonomous improvement of the individuals(at the mutation phase),and introduces dynamism in the crossover probability.Each subpopulation begins with a very low value of crossover probability,and then varies with the change of the current generation number and the search performance on recent generations.This mechanism helps prevent premature convergence.In this research,the effectiveness of this technique is tested using three well-known routing problems,i.e.,the traveling salesman problem(TSP),capacitated vehicle routing problem(CVRP),and vehicle routing problem with backhauls(VRPB).MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.展开更多
文摘Mobile applications are being used in a great range of fields and application areas. As a result, many research fields have focused on the study and improvement of such devices. The current Smartphones are the best example of the research and the evolution of these technologies. Moreover, the software design and development is progressively more focused on the user; finding and developing new mobile interaction models. In order to do so, knowing what kind of problems the users could have is vital to enhance a bad interaction design. Unfortunately, a good software quality evaluation takes more time than the companies can invest. The contribution revealed in this work is a new approach to quality testing methodology focused on mobile interactions and their context in use where external capturing tools, such as cameras, are suppressed and the evaluation environments are the same as the user will use the application. By this approach, the interactions can be captured without changing the context and consequently, the data will be more accurate, enabling the evaluation of the quality-in-use in real environments.
文摘We propose a multi-crossover and adaptive island based population algorithm(MAIPA).This technique divides the entire population into subpopulations,or demes,each with a different crossover function,which can be switched according to the efficiency.In addition,MAIPA reverses the philosophy of conventional genetic algorithms.It gives priority to the autonomous improvement of the individuals(at the mutation phase),and introduces dynamism in the crossover probability.Each subpopulation begins with a very low value of crossover probability,and then varies with the change of the current generation number and the search performance on recent generations.This mechanism helps prevent premature convergence.In this research,the effectiveness of this technique is tested using three well-known routing problems,i.e.,the traveling salesman problem(TSP),capacitated vehicle routing problem(CVRP),and vehicle routing problem with backhauls(VRPB).MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.
