Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. While ...
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The authors study on the hand gesture discernment based on the surface electromyogram of forearm. In order to discern finger shapes of the rock-paper-scissors, genetic programming technique is applied to establish the...
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The authors study on the hand gesture discernment based on the surface electromyogram of forearm. In order to discern finger shapes of the rock-paper-scissors, genetic programming technique is applied to establish the optimum classification algorithm of hand gestures by composing of arithmetic functions. We measur myoelectric potential signals of forearm related to rock-paper-scissors, and applies them to genetic evolution of hand gesture classification. We also evaluated the effects of the target number of nodes, crossover rate, mutation rate of GP parameters. Realtime hand gesture identification experiments are carried out and the typical hand gestures are actually distinguished in accuracy of 99%.
Monetary policy affects the economy with long and variable lags, and for this reason policy-makers require reliable forecasts of economic activity. Hence, forecasts of real GDP growth have become more and more necessa...
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Monetary policy affects the economy with long and variable lags, and for this reason policy-makers require reliable forecasts of economic activity. Hence, forecasts of real GDP growth have become more and more necessary. Haiming Guo (2006) proposed a new modified ARIMA model and used it to forecast the GDP growth of China from 1978 to 2004. Their experimental data show that the modified ARIMA model could provide more accurate forecasts than conventional ARIMA. However, all these models are linear. In this paper, we propose a new genetic programming method to forecast the GDP time series of China, United States and Japan from 1980 to 2006. Experimental results show that genetic programming yield statistically lower forecast errors for the year- over-year GDP data relative to modified linear ARIMA models. Moreover, we use the proposed method to forecast the future GDP growth of China, United States and Japan from 2007 to 2020, and we surprisingly find that the GDP of Japan fluctuates periodically, however the GDP of China and United States increases stably in the near future. According to the predicted data we can see that the GDP of China will exceed the GDP of Japan for the first time in 2020 or so.
Content-targeted advertising is an ads placement technique which associates ads to a web page relative to (based on) the content of the web page (web page content). It introduces a challenge about how to settle the co...
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Content-targeted advertising is an ads placement technique which associates ads to a web page relative to (based on) the content of the web page (web page content). It introduces a challenge about how to settle the conflict of interests by selecting advertisements that are relevant to the users but also profitable to the advertisers and the publishers. This paper proposes an approach to associate ads with web pages using genetic programming (GP). GP is an extension of genetic algorithm in which the individual is not a stream of character but rather a program (function). This work is done in two stages. In the first stage, GP is used to learn a ranking function which leverages the structural and non structural information of the ads. The structural parts of the ads are the title and description. These are the parts that are shown when an ad is placed in a web page. The non-structural part is the set of keywords assigned to the ads. This part is used by the advertisers to determine what topic of the web page content should be to have the ads shown on it. The ranking function produced in the first stage is then used to rank ads given content of a web page in the second stage, the content-targeted advertising system. The experiment result showed that the ranking function effectiveness is just a little below the baseline method but its time efficiency is far better than the baseline at almost 12 times better. In spite of its effectiveness deficiency, the ranking function is still more suitable for content-targeted advertising system. The experiment result also proved that the mutation genetic operation contributes to the result of GP learning by creating a better-performed ranking function. The ranking function generated from GP learning which used mutation genetic operation is 0.11 more effective than the ranking function generated from GP which did not used mutation genetic operation.
Web site defacement, the process of introducing unauthorized modifications to a Web site, is a very common form of attack. Detecting such events automatically is very difficult because Web pages are highly dynamic and...
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Web site defacement, the process of introducing unauthorized modifications to a Web site, is a very common form of attack. Detecting such events automatically is very difficult because Web pages are highly dynamic and their degree of dynamism may vary widely across different pages. In this paper we propose a novel detection approach based on genetic programming (GP), an established evolutionary computation paradigm for automatic generation of algorithms. What makes GP particularly attractive in this context is that it does not rely on any domain-specific knowledge, whose description and synthesis is invariably a hard job. In a preliminary learning phase, GP builds an algorithm based on a sequence of readings of the remote page to be monitored and on a sample set of attacks. Then, we monitor the remote page at regular intervals and apply that algorithm, which raises an alert when a suspect modification is found. We developed a prototype based on a broader Web detection framework we proposed earlier and we tested our approach over a dataset of 15 dynamic Web pages, observed for about a month, and a collection of real Web defacements. We compared the results to those of a solution we developed earlier, whose design embedded a substantial amount of domain specific knowledge, and the results clearly show that GP may be an effective approach for this job.
Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes t...
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Breast density is widely used as an initial indicator of developing breast cancer. At present, current classification methods for mammographic density usually require manual operations or expert knowledge that makes them expensive in real-time situations. Such methods achieve only moderate classification accuracy due to the limited model capacity and computational resources. In addition, most existing studies focus on improving classification accuracy using only raw images or the entire set of original attributes and remain unable to identify hidden patterns or causal information necessary to discriminate breast density classes. It is challenging to find high-quality knowledge when some attributes defining the data space are redundant or irrelevant. In this study, we present a novel attribute construction method using genetic programming (GP) for the task of breast density classification. To extract informative features from the raw mammographic images, wavelet decomposition, local binary patterns, and histogram of oriented gradients are utilized to include texture, local and global image properties. The study evaluates the goodness of the proposed method on two benchmark real-world mammographic image datasets and compares the results of the proposed GP method with eight conventional classification methods. The experimental results reveal that the proposed method significantly outperforms most of the commonly used classification methods in binary and multi-class classification tasks. Furthermore, the study shows the potential of G P for mammographic breast density classification by interpreting evolved attributes that highlight important breast density characteristics.
genetic programming is a branch of genetic algorithms. The main difference between genetic programming and genetic algorithms is the representation of the solution. genetic programming creates computer programs in LIS...
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genetic programming is a branch of genetic algorithms. The main difference between genetic programming and genetic algorithms is the representation of the solution. genetic programming creates computer programs in LISP computer language as the solution whereas genetic algorithms create a string of numbers that represent the solution (see Holland, J.H., 1975). The new way of representation used in GP encouraged researchers to use it in solving design problems where the size and shape of the solution is unknown (see Koza, J.R., 1992). Curve fitting problems used to be solved by assuming the equation shape or degree then searching for the parameter values as done in regression techniques. This paper demonstrates that curve fitting problems can be solved using GP without need to assume the equation shape. An object oriented technique has been used to design and implement a general purpose GP engine.
A concept of artificial supervisor of multi-task real-time object-oriented system is introduced. Next, a procedure for automatic creation of artificial supervisors is presented. The procedure is based on developmental...
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A concept of artificial supervisor of multi-task real-time object-oriented system is introduced. Next, a procedure for automatic creation of artificial supervisors is presented. The procedure is based on developmental genetic programming. As an input data, UML diagrams are used. A representative example of creation of a supervisor of building a house illustrates the procedure. The efficiency of the procedure from various points of view and comparison considerations are given.
genetic programming (GP) has proved useful in optimization problems. The way of representing individuals in this methodology is particularly good when we want to construct decision trees. Decision trees are well suite...
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genetic programming (GP) has proved useful in optimization problems. The way of representing individuals in this methodology is particularly good when we want to construct decision trees. Decision trees are well suited to representing explicit information and relationships among parameters studied. A set of decision trees could make up a decision support system. In this paper we set out a methodology for developing decision support systems as an aid to medical decision making. Above all, we apply it to diagnosing the evolution of a burn, which is a really difficult task even for specialists. A learning classifier system is developed by means of multipopulation genetic programming (MGP). It uses a set of parameters, obtained by specialist doctors, to predict the evolution of a burn according to its initial stages. The system is first trained with a set of parameters and results of evolutions which have been recorded over a set of clinic cases. Once the system is trained, it is useful for deciding how new cases will probably evolve. Thanks to the use of GP, an explicit expression of the input parameter is provided. This explicit expression takes the form of a decision tree which will be incorporated into software tools that help physicians In their everyday work.
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