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Development of Optimization Design Software for Bevel Gear Based on Integer Serial Number Encoding Genetic Algorithm  [cached]
Xiaoqin Zhang,Yu Rong,Jingjing Yu,Liling Zhang
Journal of Software , 2011, DOI: 10.4304/jsw.6.5.915-922
Abstract: Bevel gear drive is widely used, quality of which not only affects its own transmission performance, size and weight, but also has some impact on the machine's performance. This paper introduces optimization design software for bevel gear, in which automatic optimization design is realized. In the paper mathematical model, programming of design data and realization of optimization design based on genetic algorithm are described in detail. The paper proposed integer serial number encoding genetic algorithm, which effectively deals with continuous and discrete variable optimization problem and reduces the code length of the string to improve the encoding and decoding efficiency, no invalid solution or duplicate solutions.
Objective Variation Simplex Algorithm for Continuous Piecewise Linear Programming  [PDF]
Yu Bai,Zhiming Xu,Xiangming Xi,Shuning Wang
- , 2017, DOI: 10.1109/TST.2017.7830897
Abstract: This paper works on a modified simplex algorithm for the local optimization of Continuous PieceWise Linear (CPWL) programming with generalization of hinging hyperplane objective and linear constraints. CPWL programming is popular since it can be equivalently transformed into difference of convex functions programming or concave optimization. Inspired by the concavity of the concave CPWL functions, we propose an Objective Variation Simplex Algorithm (OVSA), which is able to find a local optimum in a reasonable time. Computational results are presented for further insights into the performance of the OVSA compared with two other algorithms on random test problems.
Multi-objective Optimization of RFID Network Based on Genetic Programming  [PDF]
Pan Weijie,Li Shaobo,Xie Qingsheng,Yang Guanci
Information Technology Journal , 2011,
Abstract: With the widespread application of RFID tags, the layout of RFID readers under guaranteed the rate of coverage, RFID network load balance and communication quality which becomes a major focus of current research on RFID network. Present study analyzes the characteristics of RFID network and the disadvantages of current optimization methods on readers network, by establishing the mathematical optimization model of RFID network, a kind of method that multi-objective optimization of RFID network based on Genetic Programming is proposed and the evolutional topological operators, terminal set and fitness functions are designed. Finally, it realized the module of the multi-objective optimization algorithm, the number of readers and the layout of readers automatic optimization. The experimental results show that it has higher efficiency, faster convergence rate and good accuracy. It can keep well balance between topology and parameter search. This research has important reference value in the theory and practice.
Application of Niche Technology to Genetic Programming
小生境技术在遗传规划中的应用

LIU Guo-Wei,CHANG Xin-Gong,
刘国炜
,常新功

计算机系统应用 , 2011,
Abstract: To improve the performance of genetic programming algorithm,the niche technology used in genetic algorithm is applied to genetic programming.It is improvement of genetic programming algorithm,which is called NGP in the next text.First,the algorithm fits the data with the original training set.Second,it tracks the extreme points of the fitting function,and according to the dimensions of the fitting function,calculate the extreme points' Euclidean distance in the independent variable dimension and order it.Th...
Computational Complexity Analysis of Multi-Objective Genetic Programming  [PDF]
Frank Neumann
Computer Science , 2012,
Abstract: The computational complexity analysis of genetic programming (GP) has been started recently by analyzing simple (1+1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria influences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.
Continuous On-line Evolution of Agent Behaviours with Cartesian Genetic Programming  [PDF]
Davide Nunes,Luis Antunes
Computer Science , 2014,
Abstract: Evolutionary Computation has been successfully used to synthesise controllers for embodied agents and multi-agent systems in general. Notwithstanding this, continuous on-line adaptation by the means of evolutionary algorithms is still under-explored, especially outside the evolutionary robotics domain. In this paper, we present an on-line evolutionary programming algorithm that searches in the agent design space for the appropriate behavioural policies to cope with the underlying environment. We discuss the current problems of continuous agent adaptation, present our on-line evolution testbed for evolutionary simulation.
Multi-Objective Genetic Programming Projection Pursuit for Exploratory Data Modeling  [PDF]
Ilknur Icke,Andrew Rosenberg
Computer Science , 2010,
Abstract: For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality theorem, the number of samples needed for a classification task increases exponentially as the number of dimensions (variables, features) increases. On the other hand, it is costly to collect, store and process data. Moreover, irrelevant and redundant features might hinder classifier performance. In exploratory analysis settings, high dimensionality prevents the users from exploring the data visually. Feature extraction is a two-step process: feature construction and feature selection. Feature construction creates new features based on the original features and feature selection is the process of selecting the best features as in filter, wrapper and embedded methods. In this work, we focus on feature construction methods that aim to decrease data dimensionality for visualization tasks. Various linear (such as principal components analysis (PCA), multiple discriminants analysis (MDA), exploratory projection pursuit) and non-linear (such as multidimensional scaling (MDS), manifold learning, kernel PCA/LDA, evolutionary constructive induction) techniques have been proposed for dimensionality reduction. Our algorithm is an adaptive feature extraction method which consists of evolutionary constructive induction for feature construction and a hybrid filter/wrapper method for feature selection.
Downscaling near-surface atmospheric fields with multi-objective Genetic Programming  [PDF]
Tanja Zerenner,Victor Venema,Petra Friederichs,Clemens Simmer
Physics , 2014,
Abstract: The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally often more expensive atmospheric models, for instance, are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn equations or short programs that reconstruct the fine-scale fields (e.g., 400 m resolution) of the near-surface atmospheric state variables from the coarse atmospheric model output (e.g., 2.8 km resolution). Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Using the Strength Pareto Approach for multi-objective fitness assignment allows us to consider multiple characteristics of the fine-scale fields during the learning procedure.
Service composition model and implementation based on genetic programming
基于遗传规划的服务匹配算法研究

DENG Lei,WU Jian,MA Man-fu,HU Zheng-guo,
邓磊
,吴健,马满福,胡正国

计算机应用 , 2006,
Abstract: Based on the feature abstraction in semantic service,the elementary service can be abstracted as action rule unit(ARU).So,a services composition model based on action planning was proposed.In order to satisfy the functional and nonfunctional needs,a service matching algorithm based on genetic programming(GP-SMA),was introduced by integrating with the variable and layered structure of genetic programming.The action rules among these ARU guide the planning process.Simulation results show that this algorithm exhibits some good performances such as find-best ability and efficiency in the selection space of associated service.
Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance  [PDF]
Pu Wang,Michael Emmerich,Rui Li,Ke Tang,Thomas Baeck,Xin Yao
Computer Science , 2013,
Abstract: ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs.
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