This paper introduces an improved genetic algorithm (GA) to solve the job-shop scheduling problem. A typical case is illustrated to report on how it uses the encoding method to address the problem and to work out the optimal schedules. This method has been tested with ten groups of samples generated randomly. The results show its outperformance to the traditional GA on solving the same problem in terms of best, worst and average ability.

This paper investigates the network security problem which is complex to evaluate. The key of this evaluation is to establish the factors and weights. Traditional methodologies cannot get rid of the key issues, resulting incorrect evaluation. In order to improve the accuracy, this paper proposes an evaluation model which is based on analytical hierarchy process (AHP) to evaluate the network security in the real-life background. There are several steps. First, evaluation factors are set up through Delphi method. Second, the weights are determined by AHP. Third, the evaluation is executed. Finally, the real-life cases are used to verify the model. The verification indicates that AHP enables to evaluate the network security efficiently in terms of objectiveness, high accuracy and comprehensiveness.

This paper focuses on an important research topic in data mining (DM) which heavily replies on the association rules. In order to deal with the maintenance issues within the background of the static transaction database, there are some minor changes to minimum support and confidence coefficient. A novel algorithm based on incremental updated is proposed, which is termed as NIUA (Novel Incremental Updating Algorithm). IUA uses association rules to mining the database, aiming at finding the potential information or finding the reasons from massive data.