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In order to solve the problem of "information overflow" in e-learning, an algorithm based on Hebbian learning law is proposed for constructing self-organized communities which can automatically group e-learners according to their learning interests. Unlike filtering methods,this algorithm takes into consideration of the distributed open environment of e-learning. This paper designed a peer-to-peer...
A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms...
A hybrid algorithm based on Extremal Optimization (EO) with adaptive levy mutation and Differential Evolution (HEODE) was proposed in this paper. It applied the idea of combination mechanism of global and local search. In the process of the global search, DE is an evolutionary algorithm based on the difference in group that can quickly approach a approximate optimal solution. During the local search,...
Recently in-depth analysis of network security vulnerability must consider attacker exploits not just in isolation, but also in combination. The general approach to this problem is to compute attack graphs using a variety of graph-based algorithms. However, such methods generally suffer the exponential state space problem. Therefore, this paper brings forward two conceptions of vulnerability correlation...
For measuring the uncertainty of behavior, the average rough coverage doesn't consider the difference among middle learning stages in reinforcement learning. To address this problem, a novel measure model based on generalized approximation spaces is proposed. In this study, uncertainty is regarded as the local feature of a state and used to guide future learning. Data-driven Q-learning based this...
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