Selecting a suitable kernel for relevance vector machine is one of most challenging aspects of successfully using this learning tool. Efficiently automating the search for such a kernel is therefore desirable. This paper proposes a datadriven kernel function construction and optimization method, which combines genetic programming(GP) and relevance vector regression to evolve an optimal or near-optimal kernel function, named GP-Kernel. The evolved kernel is compared to several widely used kernels on several regression benchmark datasets. Empirical results demonstrate that RVM using such GP-Kernel can outperform or match the best performance of standard kernels.
Abstract. In this article, we present a new approach which incorporates fuzzy logic with data-driven crowd simulation method. Behavior rules are derived from the state-action samples obtained from crowd videos by MLFE algorithm. The derived rules complement the disadvantages of rule-based approach in the situation where the crowd behavior can't be reduced to rules, i.e., calibrate the behaviors produced by rule-based approach. During a simulation, the new derived rules can be combined with pre-defined ones. Then, the compositive rules are treated in the overview of fuzzy logic to simulate various crowd behaviors. It is noticeable that the derived rules can be used independently for data-driven crowd simulation or be incorporated with predefined rules. The advantages of our approach are obvious: interoperability, universality and behavior's diversity.
This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality.
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