2888098
Evolutionary interactive music composition
This paper proposes the CFE framework---Composition, Feedback, and Evolution---and presents an interactive music composition system. The system composes short, manageable pieces of music by interacting with users. The most important features of the system include creating customized music according to the user preference and providing the facilities specifically designed for producing large amounts of music. We present the structure as well as the implementation of the system and the auxiliary functionalities that enhance the system. We also introduce the auto-feedback test with which we verify and evaluate the interactive music composition system.
34645664
Applying Family Competition to Evolution Strategies for Constrained Optimization
This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.
9845542
Back Propagation with Randomized Cost Function for Training Neural Networks
A novel method to improve both the generalization and convergence performance of the back propagation algorithm (BP) by using multiple cost functions with a randomizing scheme is proposed in this paper. Under certain conditions, the randomized technique will converge to the global minimum with probability one. Experimental results on benchmark Encoder-Decoder problems and the NC2 classification problem show that the method is effective in enhancing BP's convergence and generalization performance
535520
Detection of Entity Mentions Occuring in English and Chinese Text
In this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language independent system. It is optimal in the sense that all categorical constraints are simultaneously considered. The system is compact and easy to develop and maintain, since only a single set of features and classifiers are needed to be designed and optimized. It is implemented using one-versus-all support vector machine (SVM) classifiers and a number of feature extractors at several linguistic levels. SVMs are well known for their ability to handle a large set of overlapping features with theoretically sound generalization properties. Data sparsity might be an important issue as a result of a large number of classes and relatively moderate training data size. However, we report results that the integrated system performs as good as a pipelined system that decomposes the problem into a few smaller sub-tasks. We conduct all our experiments using ACE 2004 data, evaluate the systems using ACE metrics and report competitive performance.
39358979
Multi-agent based dynamic scheduling for a flexible assembly system
This paper proposes a multi-agent based dynamic scheduling approach for a flexible assembly system. We first introduce a flexible control system developed by Intelligent Robotics and Automation Laboratory in National Taiwan University. Based on that control system, the agents can communicate with each other conveniently. A generic agent architecture is proposed to model the pieces of equipment in the flexible assembly system. With a distributed architecture, the agents make their scheduling decisions using their local rule base. The agents acquire the resources following the distributed resource allocation protocol. The scheduling complexity is reduced to meet the real-time response requirement in the applications for flexible automated production. The present work is applied to the experimental robotized flexible assembly system in the above laboratory.
18807121
Stochastic voting algorithms for Web services group testing
This paper proposes a stochastic voting for testing a large number of Web services (WS) under group testing. In the future, a large number of WS would be available and they need to be tested and evaluated in real time. While numerous test input generation techniques are available to generate test inputs, the oracle or the expected output of these test input is often difficult to obtain. One way to obtain the oracle in this case is to give the same input to multiple WS and to establish the oracle by a majority voting. This is based on the assumption that faulty WS often would not produce consistent results, and thus if a majority can be reached, the oracle can be established statistically. However, even correct WS may still produce slightly different outputs, and thus the majority-voting scheme must be carefully designed to distinguish correct but slightly variant output from truly incorrect output. This paper proposes a hierarchical classification based on simulated annealing and multi-dimensional Chi-square statistical techniques to analyze data to see if a majority can be reached. The algorithm is evaluated by a comprehensive simulated data as well as actual data. The data show that the proposed algorithm is effective even in a difficult situation where clusters of WS produce clusters of output.
2396817
Automatic Pattern-Taxonomy Extraction for Web Mining
In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.