Исследование возможностей генетического алгоритма для извлечения релевантных прецедентов в системах поддержки принятия решений

В статье рассматриваются достоинства и недостатки прецедентного подхода для представления знаний о предметной области. В качестве одного из недостатков выделяется недостаточное быстродействие извлечения прецедентов в режиме реального времени, а также недостаточная релевантность извлекаемых прецедентов решаемой задаче. Для решения указанных проблем в настоящей работе мы предлагаем применение генетического алгоритма для извлечения прецедентов. Рассматривается формализация задачи, а также алгоритм ее решения. Приводятся результаты тестирования алгоритма. В заключении приводятся перспективы применения метода для адаптации прецедентов.Работа поддержана грантом Министерства образования и науки РФ в рамках проектной части Госзадания, проект № 2.2327.2017/ПЧ «Интеграция моделей представления знаний на основе интеллектуального анализа больших данных для поддержки принятия решений в области программной инженерии»

Repetitive mutations in genetic algorithm for software test data generations

Generating test data is the most important part of dynamic software testing. One of the white box testing techniques is path coverage testing. Genetic Algorithm (GA) has proven to be an important method in generating test data for automatic path coverage testing. However, to satisfy path coverage testing, GA’s operation of a single mutation generates test data that covers the same path in a single generation, hence resulting in path coverage duplication, which negatively increases the number of iterations. Therefore, this study proposes a repetitive mutation for GA in order to eliminate path coverage duplication and reduce the number of iterations for test data generations in path coverage testing. The study was conducted in three phases. First, the limitations of existing mutation techniques used in GA to generate test data for path coverage testing were analysed. Then, a repetitive mutation technique for GA was designed and implemented in a numerical simulation using C++ language. Finally, the evaluation phase that compares the outcome of the proposed technique against existing studies in terms of the number of iterations for test data generations. The findings show that the proposed repetitive mutation technique outperformed the single mutation technique by reducing the number of iterations to more than 50 percent for test data generations. The study has revealed the importance of mutation in generating test data and how it can be harnessed to quickly guide GA in producing solutions. In addition, the proposed repetitive mutation in GA can contribute to developing an adaptive GA testing tool

A Protocol and Tool for Developing a Descriptive Behavioral Model

Fuzzy rules have been used to model complex human behavior in order to develop sophisticated industrial control systems. The use of fuzzy rules to create a behavioral model provides a quantitative basis for discussing the contribution of elements of the model to theories about the behavior. The application of a protocol and tool simplifies the development of a behavioral model from observational data. Extraction of a high level, linguistic behavioral model from the observational data is used to discover knowledge about the data. Tuning of the model is accomplished by parameter optimization through the adjustment of membership functions using the genetic fuzzy, self-adaptive system. A case study demonstrating the use of the protocol and tool is presented. In the study, a behavioral model is developed that integrates the analysis of the observational data with Social Network Analysis. The integrated behavioral model provides an effective platform for a quantitative analysis of the activities impacting behavior.  M.S

Using Case-Based Reasoning for Simulation Modeling in Healthcare

The healthcare system is always defined as a complex system. At its core, it is a system composed of people and processes and requires performance of different tasks and duties. This complexity means that the healthcare system has many stakeholders with different interests, resulting in the emergence of many problems such as increasing healthcare costs, limited resources and low utilization, limited facilities and workforce, and poor quality of services. The use of simulation techniques to aid in solving healthcare problems is not new, but it has increased in recent years. This application faces many challenges, including a lack of real data, complicated healthcare decision making processes, low stakeholder involvement, and the working environment in the healthcare field. The objective of this research is to study the utilization of case-based reasoning in simulation modeling in the healthcare sector. This utilization would increase the involvement of stakeholders in the analysis process of the simulation modeling. This involvement would help in reducing the time needed to build the simulation model and facilitate the implementation of results and recommendations. The use of case-based reasoning will minimize the required efforts by automating the process of finding solutions. This automation uses the knowledge in the previously solved problems to develop new solutions. Thus, people could utilize the simulation modeling with little knowledge about simulation and the working environment in the healthcare field. In this study, a number of simulation cases from the healthcare field have been collected to develop the case-base. After that, an indexing system was created to store these cases in the case-base. This system defined a set of attributes for each simulation case. After that, two retrieval approaches were used as retrieval engines. These approaches are K nearest neighbors and induction tree. The validation procedure started by selecting a case study from the healthcare literature and implementing the proposed method in this study. Finally, healthcare experts were consulted to validate the results of this study