In spite of evolution of electronic techniques, a large number of applications continue to rely on the use of paper as the dominant medium. Bank checks are a widely known example. When filled by hand, the processing o...
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In spite of evolution of electronic techniques, a large number of applications continue to rely on the use of paper as the dominant medium. Bank checks are a widely known example. When filled by hand, the processing of the written information requires either a human or a special software which has intelligent abilities. This paper examines the issue of reading the amount of money written on the checks. genetic programming (GP) technique is used for dealing with this problem. A new type of input representation is proposed: histograms. Several numerical experiments with GP are performed by using large datasets taken from the MNIST benchmarking set. Preliminary results show a good behavior of the method.
Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was ...
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Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviors. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.< >
genetic programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or dist...
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ISBN:
(纸本)9781728100043
genetic programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or distributed implementations. In this paper, we focus on dynamic sampling algorithms that mostly give to GP learner a new sample each generation. In so doing, individuals do not have enough time to extract the hidden knowledge. We propose adaptive sampling which is half-way between static and dynamic methods. It is a flexible approach applicable to any dynamic sampling. We implemented some variants based on controlling re-sampling frequency that we experimented to solve KDD intrusion detection problem with GP. The experimental study demonstrates how it preserves the power of dynamic sampling with possible improvements in learning time and quality for some sampling algorithms. This work opens many new relevant extension paths.
genetic programming is an evolutionary approach known for its performance in program synthesis. However, it is not yet mature enough for a practical use in real-world software development, since usually many training ...
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In the remaining life prediction of bearings, feature extraction is crucial because it directly determines the prediction accuracy of the model. In response to this problem, this paper proposes a feature extraction me...
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ISBN:
(纸本)9781665479691
In the remaining life prediction of bearings, feature extraction is crucial because it directly determines the prediction accuracy of the model. In response to this problem, this paper proposes a feature extraction method based on genetic programming. First, the multi-dimensional features are combined into an independent feature combination in the form of a feature tree, and then an improved fitness function is designed. After many iterations, The feature combination with the highest fitness is finally output, which is called the optimization feature. Finally, the least square method is used to predict the optimization characteristic curve, and the life prediction can be carried out by combining with the failure model. Finally, the public data set of bearing full life is used to predict the remaining service life of the bearing with the optimization feature as the model, which verifies the accuracy of the algorithm prediction.
Automatic modulation classification is used to identify automatically the modulation type of an incoming signal with limited or no prior knowledge to it. Various classifier systems have been developed to solve this pr...
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Automatic modulation classification is used to identify automatically the modulation type of an incoming signal with limited or no prior knowledge to it. Various classifier systems have been developed to solve this problem. However, for certain types of modulations such as 16 QAM and 64 QAM, the classification performance under noisy condition still needs to be improved. In this paper, we propose a new AMC scheme by combining genetic programing (GP) with support vector machine (SVM) for the classification of 16 QAM and 64 QAM signals. The benchmark result shows that SVM assisted GP can produce better accuracy than some other existing methods.
We investigate the roles of insertion and deletion as mutation operators and local search operators in a tree adjoining grammar guided genetic programming (TAG3P) system (Nguyen Xuan Hoai et al., 2003). The results sh...
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We investigate the roles of insertion and deletion as mutation operators and local search operators in a tree adjoining grammar guided genetic programming (TAG3P) system (Nguyen Xuan Hoai et al., 2003). The results show that, on three standard problems, these operators work better as mutation operators than the more standard sub-tree mutation originally used in (Nguyen Xuan Hoai et al., 2003, 2004). Moreover, for some problems, insertion and deletion can act effectively as local search operators, allowing TAG3P to solve problems with very small population sizes.
genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Comparative studies show ...
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genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Comparative studies show that the new model reduction technique outperforms the conventional H 2 -norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via genetic programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PI λ D μ controllers.
Focuses on how a method for automated programming (i.e., genetic programming) applies in the computer-aided discovery of algorithms that enhance and extract features from remotely sensed images. Highlighted as a case ...
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Focuses on how a method for automated programming (i.e., genetic programming) applies in the computer-aided discovery of algorithms that enhance and extract features from remotely sensed images. Highlighted as a case study is the use of this method in the problem of extracting pressure ridge features from ERS-1 SAR imagery; a problem for which there has been no known satisfactory solution.
Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant...
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Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying genetic programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the Web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is even comparable to those obtained by support vector machine.
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