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Instance coreference resolution is an essential problem in studying semantic web, and it is also critical for the implementation of web of data and future integration and application of semantic data. In this paper, we propose to use Memetic Algorithm (MA) to solve this instance coreference problem in a sequential stage, i.e., the instance-level matching is carried out with the result of schema-level...
Wide study and application exposes some problems of evolutionary algorithms such as premature convergence and poor performance in convergence. In order to overcome these issues, this paper proposes an adaptive co-evolutionary algorithm based on genotypic diversity measure, where adaptive selection, mutation and substitution operators are designed to realize cooperative search among operators and dynamic...
In the real world, many optimization problems are dynamic constrained multi-objective optimization problems. This requires an optimization algorithm not only to find the global optimal solutions under a specific environment but also to track the trajectory of the varying optima over dynamic environments. To address this requirement, a hyper rectangle search based particle swarm algorithm is proposed...
PAM (Partitioning Around Medoids) was one of the first k-medoids algorithms. It attempts to determine k partitions for n objects. In the parallel particle swarm optimization, the number of particle is generally not too much. Therefore, PAM is used to divide the swarm is a best choises. This can make not only the location of particles within the same sub-swarm be in the relative concentrative, but...
For the task scheduling problem in the heterogeneous grid environment, a security benefit function is constructed by considering the task scheduling demand for confidentiality, integrity and authenticity. Then, according to the history behavior of grid resource nodes, node's credibility dynamic evaluation method is proposed by using the weighted function. Based on these, a new grid task security scheduling...
Differential evolution (DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. This paper proposed a multiobjective differential evolutionary algorithm based on opposite operation. Firstly, in the initialization of the algorithm, the opposite points of randomly generated individuals are calculated in order to make the initial population better....
The convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to...
In this paper, a dynamical particle swarm algorithm with dimension mutation is proposed. First, we design a dynamically changing inertia weight based on the degree of both the particle diversity and the improvement of the best solutions in the successive generations. By using this inertia weight the algorithm can more easily keep the diversity of the population, improve the convergent speed. Second,...
Studying convergence rate of a genetic algorithm is a very important but a nontrivial task. The more accurate estimation of convergence rate for genetic algorithms can be used to design the more efficient control parameters of algorithms, and to point out the correct direction to improve the algorithms. Moreover, one good measure of convergence rate can be used to judge the efficiency of different...
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