遗传规划在测量数据拟合中的应用

针对测量数据问题，提出了一种根据遗传规划来寻求最匹配曲线的新方法。与传统的曲线拟合方法相比，该方法只需给定数据点及允许误差即可得到匹配的曲线方程。对于自由曲线用传统的方法是无法拟合的，而遗传规划却可以轻而易举地做到。     

基于蚁群的数据点NURBS曲面快速拟合研究

研究数据点的NURBS曲面拟合问题,提高拟合速率。针对所要拟合的数据点分布散乱,传统的基于遗传算法多次迭代,造成曲面拟合速率不高的问题。为解决上述问题,提出一种基于蚁群的数据点NURBS曲面拟合算法。通过采用蚁群寻址算法搜索出控制顶点和边界数据点集,计算曲面的权因子后完成NURBS曲面的拟合,并使用蚁群算法对拟合曲面进行优化,避免了传统方法多代遗传迭代造成的拟合速率不高的问题。实验表明,这种方法能够快速完成散乱数据点的NURBS曲面拟合,并且具有一定的拟合效率,取得了满意的结果。     

小生境技术在遗传规划中的应用

为了提高遗传规划算法的性能,把遗传算法中的小生境技术运用到遗传规划中,提出了改进的遗传规划算法（NGP）。该算法首先对原始训练集进行数据拟合,然后应用小生境技术跟踪拟合函数的极值点,并根据拟合函数的维数的不同,分别计算极值点在自变量维上的欧氏距离并排序,选取欧式距离较大且数量不超过原始训练集10%的极值点,加入到原始训练集中作为新的训练集,最后用遗传规划算法处理新训练集。在符号回归实验中对NGP 的准确率进行了测试,说明了该算法的准确性和有效性。     

基于遗传神经网络的传感器输出特性拟合􀀁

针对最小二乘法和分段线性化等传统方法的不足,提出了解决传感器输出特性拟合和在线标定问题的遗传神经网络算法,实验验证了其有效性,本文的论述不但可以用于传感器,而且可以用于其它类似系统。     

基于协同进化遗传算法的模型拟合研究

普通遗传进化算法在解决模型拟合问题中,建模与优化顺序结构时优化效果有限、拟合速度慢、稳定性低。针对上述问题,提出基于协同进化遗传算法的模型拟合算法。该算法将建模与优化问题抽象成多种群间协同进化,通过种群间整体的适应度值交换,将种群关联起来,扩大智能算法建模过程中参数优化的时空作用范围。各种群间含有不同基因表达,在解决局部问题时具有自包含性,有利于更好地发挥各智能算法(遗传算法、遗传规划)的优势。实验结果表明,该算法的稳定性和收敛速度优于传统遗传进化算法。     

遗传规划和进化策略混合算法及应用

文中把函数拟合建模看作是模型结构和参数的优化搜索过程,将遗传规划和进化策略结合起来对函数拟合的结构和参数共存且相互影响的复杂解空间进行全局最优搜索实现拟合结构和参数的共同识别。克服了传统的函数拟合完全依赖于数据、精度低、结构与参数分别确定这样一“串行”计算结构等缺陷。实验数据表明,该方法得到的拟合函数比传统方法得到的拟合函数,具有较高的精度和推广预测能力。     

基于分布式粗粒度并行计算的遗传规划算法研究

针对经典遗传规划算法(CGP)存在容易早熟收敛、运行效率低的缺陷,提出一种将分布式计算与遗传规划算法结合的计算模型.该模型利用个体迁移策略实现对种群的优化,克服易早熟的缺陷.并且采用分布式计算能够有效地节省算法的运行时间.最后通过对语音数据预测误差的比较,验证了改进后算法的有效性.实验表明,基于分布式粗粒度并行计算的遗传规划算法(CGGP)计算性能优于经典遗传规划算法(CGP).     

基于遗传分枝定界算法的Flow-shop调度问题

Flow shop调度问题属于NP难题,传统的方法很难求出精确最优解,提出了一种遗传分枝定界算法,即在遗传算法中引入分枝定界算法保持对优化解有贡献的工件部分顺序,求解3机Flow shop调度问题,该算法与常用的遗传局部算法和遗传动态规划算法类似,用随机方法测试例子,与目前著名的Taillard的禁忌搜索算法和Reeves的遗传算法两种改进算法进行比较,大量的数据实验证实了遗传分枝定界算法的有效性。     

协同进化理论及其在施肥模型中的应用

采用数理统计方法进行施肥模型构造,由于受到固定的数学结构的限制,导致有一些实验结果因不能被模型拟合而被舍弃,造成了一些数据的浪费。针对这些问题,提出了基于协同进化理论的施肥模型构建算法,将模型构建问题分解为模型结构构建与模型参数优化两个子问题,并将这两个子问题抽象成多种群间协同进化。使用遗传规划算法进行模型结构构建,使用遗传算法对模型参数进行优化,两个过程协同进行。实验结果表明,该算法能够在历史实验数据的基础上自动生成动态模型,同时具有较好的准确度。     

遗传规划在符号回归中的应用

遗传规划(GP)是一种基于达尔文进化理论的数学规划方法。讨论了GP在符号回归中的应用。与传统的数据拟合方法相比,GP不必给出拟合函数的形式,同时,在初始群体足够大而且交叉和变异概率设置合理的情况下,不会陷入局部优化,具有更广泛的适用性。对于不给定函数形式的曲线拟合,GP可以自动得到曲线的函数形式及其参数大小,避免了传统方法的缺陷。通过具体的应用实例,说明了GP在测量数据处理中的应用。     

Polynomial genetic programming for response surface modeling Part 1: a methodology

The second-order polynomial is commonly used for fitting a response surface but the low-order polynomial is not sufficient if the response surface is highly nonlinear. Based on genetic programming (GP), this paper presents a method with which high-order smooth polynomials, which can model nonlinear response surfaces, can be built. Since in many cases small samples are used to fit the response surface, it is inevitable that the high-order polynomial shows serious overfitting behaviors. Moreover, the high-order polynomial shows infamous wiggling, unwanted oscillations, and large peaks. To suppress such problematic behaviors, this paper introduces a novel method, called directional derivative-based smoothing (DDBS) that is very effective for smoothing a high-order polynomial.The role of GP is to find appropriate terms of a polynomial through the application of genetic operators to GP trees that represent polynomials. The GP tree is transformed into the standard form of a polynomial using the translation algorithm. To estimate the coefficients of the polynomial quickly the ordinary least-square (OLS) method that incorporates the DDBS and extended data-set method is devised.Also, by using the classical Lagrange multiplier method, the modified OLS method enabling interpolation is presented.Four illustrative numerical examples are given to demonstrate the performance of GP with DDBS.     

基于GP2Y0A02距离测量的仿真与辅助设计

为了实现非接触距离测量,利用多个仿真与设计软件设计了采用GP2Y0A02模块的距离光学测试系统。在PROTEUS中测量GP2Y0A02电压值,并在Matlab中采用多元线性回归建立GP2Y0A02电压与距离的拟合曲线,最后在开发的测量程序中利用拟合曲线求解得到距离量。测试结果表明,对于非线性位置测量采用适当数据处理方法能得到满意的测量精度。     

A Bayesian nonparametric approach to modeling motion patterns

The most difficult??and often most essential??aspect of many interception and tracking tasks is constructing motion models of the targets. Experts rarely can provide complete information about a target??s expected motion pattern, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, Bayesian nonparametric models of target motion are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides an adaptive representation for each individual motion pattern, while the DP prior allows us to represent an unknown number of motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area.     

GP算法中适应度函数的光滑拟合与调整参数方法研究

研究了遗传程序设计(GP)算法中适应度函数的光滑拟合问题,结合LAMs(Linear association memorys)方法和HJ(Hook和Jeevs)方法两种方法,估计GP树数值权值,以减少GP树适应度值评价的计算代价.光滑拟合的好坏关键取决于调整参数的选择.提出了一种选择调整参数的新方法,同时,给出了两个数学例子,并与广义交叉实验B-样条函数仿真比较验证.     

遗传程序设计算法中选择调整参数的光滑拟合方法

对遗传程序设计(GP)算法中的适应度评价函数光滑拟合问题进行了研究,结合LAM(Linear Association Memory)和HJ(Hook和Jeevs)两种方法,估计GP树数值权值,以减少GP树适应度值评价的计算代价。提出了一种选择调整参数的新方法,同时,给出了一个数学例子,并与广义交叉实验B一样条函数仿真比较验证。     

Accelerated Genetic Programming of Polynomials

An accelerated polynomial construction technique for genetic programming is proposed. This is a horizontal technique for gradual expansion of a partial polynomial during traversal of its tree-structured representation. The coefficients of the partial polynomial and the coefficient of the new term are calculated by a rapid recurrent least squares (RLS) fitting method. When used for genetic programming (GP) of polynomials this technique enables us not only to achieve fast estimation of the coefficients, but also leads to power series models that differ from those of traditional Koza-style GP and from those of the previous GP with polynomials STROGANOFF. We demonstrate that the accelerated GP is sucessful in that it evolves solutions with greater generalization capacity than STROGANOFF and traditional GP on symbolic regression, pattern recognition, and financial time-series prediction tasks.     

软件可靠性的GP演化建模

为解决软件可靠性模型的不一致性，摆脱传统模型多种主观假设的束缚，提出了采用遗传程序设计(GP)的演化算法建立基于软件失效间隔时间序列的软件可靠性模型。针对装甲兵工程学院的某软件测试用例进行演化建模，并对其性能指标进行了分析评价，结果验证了GP算法的可行性以及拟合、预测的有效性，从而能有效地应用于软件系统测试，保障数据的有效性。     

A genetic programming model to generate risk-adjusted technical trading rules in stock markets

Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.