We study a new approach, based on genetic programming, for generating automobile driving agents (driving rules). In a simulation environment, we develop agents for two tasks: lane departure recovery and risk avoidance...
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ISBN:
(纸本)9781509025985
We study a new approach, based on genetic programming, for generating automobile driving agents (driving rules). In a simulation environment, we develop agents for two tasks: lane departure recovery and risk avoidance lane change. The agents control a car by operating its front wheels using such observations as the distance between the car and the centerline. Our GP process generates an appropriate rule for front wheel operation. We experimentally demonstrate that our GP-based approach successfully generates an effective driving agent (rule).
A protein complex is either transient or obligate. In the organisms, proteins recognize each other with their binding sites and become transient protein complexes. Proteins perform their functions through protein-prot...
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A protein complex is either transient or obligate. In the organisms, proteins recognize each other with their binding sites and become transient protein complexes. Proteins perform their functions through protein-protein recognition. In this paper, we train a model by genetic programming with physicochemical properties information of the binding sites. The model classifies and screens the proteins of recognition interactions. The model achieves an average classification accuracy of 75%. For screening, given a protein with concave binding site and a database of proteins with convex binding site, from the database we retrieve a small set of proteins in which one of them recognizes with the query protein. In average, 65% of proteins in the database are screened out.
We investigate the behaviour of image texture classifiers generated by genetic programming. We propose techniques to understand how classifiers capture textural characteristics and for discussing the effectiveness of ...
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We investigate the behaviour of image texture classifiers generated by genetic programming. We propose techniques to understand how classifiers capture textural characteristics and for discussing the effectiveness of different classifiers. Our results show that regularities of patterns can be detected by the genetic programming method without predefined knowledge.
Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of the genetic programming (GP), so-called diffe...
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Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of the genetic programming (GP), so-called differential GP (DGP), combining GP with an automatic differentiation scheme, which could solve Hamilton-Jacobi-Bellman (HJB)/ Hamilton-Jacobi-Isaacs(HJI)/ Francis-Byrnes-Isidori (FBI) equations. Lastly, the effectiveness of a DGP based design method is demonstrated through some design examples of nonlinear systems
Many games require opponent modeling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to develop and explore models of an opponent's str...
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Many games require opponent modeling for optimal performance. The implicit learning and adaptive nature of evolutionary computation techniques offer a natural way to develop and explore models of an opponent's strategy without significant overhead. In this paper, we compare two learning techniques for strategy development in the game of Spoof, a simple guessing game of imperfect information. We compare a genetic programming approach with a look-up table based approach, contrasting the performance of each in different scenarios of the game. Results show both approaches have their advantages, but that the genetic programming approach achieves better performance in scenarios with little public information. We also trial both approaches against opponents who vary their strategy; results showing that the genetic programming approach is better able to respond to strategy changes than the look-up table based approach
Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total...
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Agile Earth Observing Satellite (AEOS) scheduling problem (AEOSSP) consists in selecting a subset of tasks from a given task set which are then scheduled on the agile satellite with the purpose of maximizing the total reward of scheduled tasks. AEOSSP is strongly NP-hard and therefore existing solution approaches mainly fall in the field of heuristics and metaheuristics. According to the no free lunch theory, it is impossible to find a single heuristic that is well-applied to any problem instance and a problem-tailored heuristic is always needed. In this paper, we propose a genetic programming based evolutionary approach (GPEA) to automatically evolve a best-suited constructive heuristic for any given AEOSSP instance. The programs (individuals) of GPEA are heuristic rules encoded as trees of mathematical functions. The fitness of the program is evaluated through mapping the mathematical function to an AEOSSP solution using a timeline-based construction algorithm. Computational results on a set of well-designed AEOSSP scenarios show that the proposed GPEA leads to a heuristic algorithm that outperforms recently published sophisticated meta-heuristic algorithm (ALNS). Additional experiments were carried out to demonstrate that the timeline based construction algorithm plays a significant role in matching time-related characteristics in comparison to four commonly used heuristic algorithms. Our results also showed that the evolved heuristic rules preserve a certain extent of generality.
In this paper an initial approach to Intelligent Control (IC) using genetic programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex syste...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
In this paper an initial approach to Intelligent Control (IC) using genetic programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.
The algorithm of genetic programming is described and applied to short-term load forecasting. For the fault in history load data, the load samples are filtered and processed generally before using, and then the load s...
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The algorithm of genetic programming is described and applied to short-term load forecasting. For the fault in history load data, the load samples are filtered and processed generally before using, and then the load series of the same time point but different days are chosen as the training sets. According to the complex expressive capacity of genetic programming, the future short-term load model of different time point is forecasted by time-sharing. This method of genetic programming can find out relevant elements to electric load data automatically, so the artificial errors in forecasting can be avoided effectively. And the future load value of each time point can be calculated with the corresponding model created. Finally, it proves that the method of genetic programming in short-term load forecasting is better through out comparison between the results forecasted by genetic programming and time series.
Five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: (1) binary decomposition, in which the problem is decomposed into a set of binary problem...
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ISBN:
(纸本)0780366573
Five alternative methods are proposed to perform multi-class classification tasks using genetic programming. These methods are: (1) binary decomposition, in which the problem is decomposed into a set of binary problems and standard genetic programming methods are applied; (2) static range selection, where the set of real values returned by a genetic program is divided into class boundaries using arbitrarily-chosen division points; (3) dynamic range selection, in which a subset of training examples are used to determine where, over the set of reals, class boundaries lie; (4) class enumeration, which constructs programs similar in syntactic structure to a decision tree; and (5) evidence accumulation, which allows separate branches of the program to add to the certainty of any given class. The results show that the dynamic range selection method is well-suited to the task of multi-class classification and is capable of producing classifiers that are more accurate than the other methods tried when comparable training times are allowed. The accuracy of the generated classifiers was comparable to alternative approaches over several data sets.
In this paper we propose an algorithm to develop an intelligent perceptual shaping function based on genetic programming (GP) in DCT domain. In digital image watermarking, robustness and imperceptibility compete with ...
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In this paper we propose an algorithm to develop an intelligent perceptual shaping function based on genetic programming (GP) in DCT domain. In digital image watermarking, robustness and imperceptibility compete with each other. In this paper we applied GP to make a trade off between these two characteristics. Here, the original image is divided into 8×8 non-overlapping blocks and the DCT coefficients in each block are sorted by means of zigzag. One AC coefficient in each block is changed according to a perceptual shaping function. This perceptual shaping function is obtained from the GP core and is dependent on average of all block coefficients and the related AC coefficient. The experimental results show that this proposed algorithm is robust against some digital image attacks such as low pass filtering, median filtering and JPEG compression. In addition the improvement in watermarked image quality also is achieved.
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