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Contend-based flower image retrieval is one of the hottest and most challenging problem in content-based image retrieval area. In this paper, a flower image retrieval method is proposed based on a memetic algorithm. The proposed method, which combines a global search strategy with a local search strategy, uses a memetic algorithm to select the optimal feature subset. Genetic algorithm is used as the global search strategy while approximate Markov blanket is adopted as the local search strategy. Primary classifiers are trained using the proposed memetic algorithm for each kind of features. The probabilities obtained by all the primary classifiers are combined together to form a mid-level feature used to train the final classifier. Experimental results show that the proposed method selects fewer number of features with better precisions and recall ratios. That also brings improvements on retrieval time.

Inverted index is a key component in a search engine. Due to the high update frequency of web pages, incremental inverted index is commonly used in modern web-based search engine. In order to improve the performance of search engine, an efficient data block and control block DB&CB structure for incremental inverted index is proposed. Theoretical analysis and experiment results show that the average complexities of posting lists' append, insert and delete operations are O(1), the average inverted index construction time is reduced by 37% comparing with the linked block data structure and the query processing performance is as good as others.

Pre-scheduling of workflows is to schedule all tasks of a workflow instance as soon as it is initialized. Based on the study of existing scheduling methods, a new optimization method of workflow pre-scheduling is proposed based on Nested Genetic Algorithm(NGA). The solutions of resource allocation and execution sequence are optimized and global optimization for a single process instance can be approached using the proposed method. Experiments are done to show the feasibility and superiority of the proposed method.

Using effective modeling ways can improve the quality of workflow models. A new role-oriented modeling way is presented to set up workflow process models based on object Petri net. The details of each role are encapsulated in a role object net. The interfaces are provided for it to communicate with other roles. The cooperative relationships of roles are described in control object nets. An example is given to show the process of the presented modeling method.

Accurate performance metric models are the key to Web capacity planning related problems. Due to the complexity of Web systems, analytical modeling without integrating the performance testing process is not enough to get accurate metric models. To integrate performance testing and analytical modeling in a systematic way, a Web performance modeling process is presented based on the methodology of learning from data. The process divides the modeling activity into several phases: constructing models and hypothetical conditions, deriving test cases, estimating parameters and validating models, etc. The scalability of a real Web community system (www.igroot.com) is studied by using the proposed process. The error of estimated saturation point is within 1 percent, the error of estimated lower bound of buckle point is within 5 percent. At last, a HTTP processing bottleneck at the architecture level is identified by correlating the model with the threads data of the Web server.

How to allocate workflow resources with cost constraint to optimize workflow time performance was studied. The average throughput time of workflow instances is used to measure the workflow time performance. A method based on queuing theory was proposed to calculate the average throughput time of workflow instances. Since the problem can be come down to an unbounded knapsack problem (UKP), an improved GA (Genetic Algorithm) suitable to solve the UKP was proposed to solve the problem. Examples were given to illustrate the feasibility and validity of the method. The results show that the algorithm has good evolution performance and can achieve or approach the optimal solutions. And compared with other allocation methods, our method performs best.

The total amount of resources in a workflow process is constrained by the cost of employment; meanwhile, the assignment of resources directly decides the average waiting and dealing time of each activity in a workflow. By optimizing the allocation of resources, it can minimize the total flow time of a workflow. Some optimization rules for minimizing the average responding time of a workflow are addressed in this paper. Then, a mathematical model of resources optimization with minimal cost time is designed, and an improved Genetic Algorithm to resolve this optimization problem is presented. The efficiency of the approaches presented is evaluated by simulated experiments and comparison with tradition GA.